CHARA

CTERISATION AND AUTOMATIC DETECTION OF LYMPH

NODES ON MR COLORECTAL IMAGES

Jeong-Gyoo Kim and J. Michael Brady

Dept. Engineering Science, Oxford University, Parks Road, Oxford, UK

Keywords:

Medical image analysis, lymph node characterisation, PDF modelling, and segmentation.

Abstract:

Colorectal cancer is the second most common cause of death in Western countries. It is often curable by

chemoradiotherapy and/or surgery; however, accurate staging has a signiﬁcant impact on patient management

and outcome. Numerous clinical reports attest to the fact that staging is not currently satisfactory, and so

more precise methods are required for effective treatment. The three major components of disease staging are

tumour size; whether or not there is distal metastatic spread; and the extent of lymph node involvement. Of

these, the latter is currently by far the hardest to quantify, and it is the subject of this paper. Lymph nodes are

distributed throughout the mesorectal fascia that envelops the colorectum. In practice, they are detected and

assessed by clinicians using properties such as their size and shape. We are not aware of any previous image

analysis approach for colorectal images that makes this subjective approach more scientiﬁc.

To aid precise staging and surgery, we have developed methods that characterises lymph nodes by extracting

implicit properties as computed from magnetic resonance colorectal images. We ﬁrst learn the probability

density function (PDF) of the intensities of the mesorectal fascia and ﬁnd that it closely approximates a Gaus-

sian distribution. The parameters of a Gaussian, ﬁtted to the PDF, were estimated and the mean intensity of

a lymph node candidate was compared with it. The ﬁtting provides an explicit criterion for a region to be

classed as a lymph node: namely, it is an outlier of the Gaussian distribution.

As a key part of this process, we need to segment the boundaries of the mesorectal fascia, which is enclosed

by two closed contours. Clinicians recognise the outer contour as thin edges. Since the thin edges are often

ambiguous and disconnected, differentiating them from neighbouring tissues is a non-trivial problem; the

surrounding tissues have no signiﬁcant difference from the mesorectal fascia in both intensity and texture. We

employed a level set method to segment three sets of objects: the mesorectal fascia, the colorectum, and lymph

node candidates. Our segmentation results led us to build a PDF and to use it for the criterion that we propose.

The whole process of implementation of our methods is automatic including the lookup of lymph candidates.

The results of clinical cases are summarised in the paper.

1 INTRODUCTION

Colorectal cancer is the second most common cause

of death in Western countries (McArdle et al., 2000)

and its incidence has increased in many Asian coun-

tries over the past few decades (Sung et al., 2005).

It is known that the disease is curable by chemora-

diotherapy and/or surgery if it is detected at an early

stage and treated appropriately; surgery is currently

the best curative therapy. Therefore, cancer staging

has a signiﬁcant impact on patient management, not

least the decision whether to proceed to surgery.

The

extent to which a (colorectal) cancer has spread is

described as its stage.

Though accurate preoperative staging is essen-

tial for planning of optimal therapy, there have been

numerous clinical reports which attest to the fact

that preoperative staging accuracy based on clinician

judgement of images is not satisfactory (Filippone

et al., 2004), and so a method for more precise stag-

ing is required for patient management and effective

treatment. Staging of the disease is based on the TNM

classiﬁcation: tumour size (T); whether or not there is

distal metastatic spread (M); and the extent of lymph

node involvement (N). Of these, the latter is currently

by far the hardest to quantify, and it is the subject of

this paper.

Lymph is a clear ﬂuid that travels through the

body’s arteries, circulates through the tissues to

403

Kim J. and Brady J. (2008).

CHARACTERISATION AND AUTOMATIC DETECTION OF LYMPH NODES ON MR COLORECTAL IMAGES.

In Proceedings of the Third International Conference on Computer Vision Theory and Applications, pages 403-412

DOI: 10.5220/0001086204030412

 SciTePress

cleanse them and keep them ﬁrm, and then drains

away through the lymphatic system. Cancer cells

may drain into nearby lymph nodes, which are bean-

shaped structures that help ﬁght against infection.

Lymph nodes are the ﬁlters along the lymphatic sys-

tem. Since the lymph nodes function to ﬁlter out

harmful cells, in particular cancer cells, this is a log-

ical place to look for cancer cells that have escaped

the original tumor and are seeking to metastasise to a

distal location. Lymph node dissection prevents can-

cer cells from further growth. The number of involved

lymph nodes strongly predicts the nature of the cancer

and the type of treatment needed to ﬁght it. For these

reasons, it is considered by clinicians to be of great

importance to assess lymph nodes in the management

of cancer.

In the case of colorectal cancer, the lymph nodes

appear as dark small blobs on magnetic resonance

(MR) images and are distributed mostly in the

mesorectum, a fatty tissue that envelops the colorec-

tum. In practice, lymph nodes are characterised by

human vision using explicit properties, such as size

and shape of lymph nodes. We are not aware of any

method of image analysis approach of colorectal im-

ages that makes this subjective approach more scien-

tiﬁc.

In this paper we develop methods to aid precise

staging and estimation of the circumferential resec-

tion margin of colorectal cancers, namely detection

and characterisation of a lymph node. The contribu-

tion of the proposed methods can be divided into two

categories: modeling an intensity probability density

function (PDF) and segmentation of non-trivial MR

images.

Section 2 describes the methods we propose; char-

acterising a lymph node by building a PDF of inten-

sity of the mesorectal fascia. For this purpose, we

need to segment the mesorectal fascia on non-trivial

images as well as lymph node candidates. Our ex-

periments are presented in section 3, and section 4

concludes and discusses our work.

2 METHODS

Our methods include lymph node detection and char-

acterisation; the two unrelated categories are com-

bined here.

We attempt to detect and analyse lymph nodes

from 3D MR colorectal images, taken at our lo-

cal hospitals by GE 1.5T magnetic resonance imag-

ing scanners with parameters of TE = 120ms, TR =

6500ms. Each 3D dataset comprises a set of 23 to 30

slices each corresponding to a 3mm to 5mm slab of

tissue.

Lymph nodes generally appear as small dark

blobs, of a distinctively low intensity compared to

the surrounding fat which has high intensity. How-

ever, lymph nodes may be heterogeneous in intensity:

such heterogeneity is generally considered evidence

for a lymph node being affected by cancer. Further-

more, since lymph nodes are small and the sampling

in MRI is large compared to their size, the partial vol-

ume effect (PVE, in which multiple tissue structures

are present in a single voxel) has a substantial im-

pact on the intensities at locations that correspond to

lymph nodes. Also, the generally bright background

is punctuated with folds in the fat, which compli-

cate it appearance. The dark blobs corresponding to

lymph appear locally similar to blood vessels, espe-

cially when viewed in a single two dimensional slide

image. We can eliminate blood vessels on the basis of

their three dimensional shapes (extended over several

slices). The consecutive slices are registered and ex-

amined if they extend over several slices; if so, they

are more likely to be blood vessels. As a result, we

may estimate locations of some possible lymph node

candidates. For each candidate we test our methods

described in this section.

Current characterisations of lymph nodes stress ei-

ther their size and/or shape (Lee et al., 1984; Brown

et al., 2003; Bond, 2006). Such criteria are observa-

tions of explicit properties, as seen on images. We

aim to extract implicit properties of lymph nodes us-

ing image analysis techniques, and which can lead to

a higher precision of lymph node criteria.

Lymph nodes are distributed in a priori unpre-

dictable locations within the mesorectum. Because

they are small, subject to the PVE, and are in un-

predictable locations, we ﬁrst determine information

about the surrounding mesorectum and use it to lo-

cate the lymph nodes. More precisely, we ﬁrst model

the PDF of the mesorectum. However, to estimate the

mesorectum PDF we ﬁrst need to isolate it from its

surrounding tissues.

The dark round object at the lower center in Figure

1 is the colorectum. The object of higher intensity that

envelops the colorectum is the mesorectum, bound by

the mesorectal fascia. The mesorectal fascia can be

recognised by human vision as a very thin low signal

intensity, however, because of the low signal to noise

and the poor sampling density, edge detectors tend to

produce disconnected edges.

To isolate the mesorectum, we need to delineate

the boundary of the mesorectal fascia and the bound-

ary of the colorectum. The difﬁculty of this segmenta-

tion task derives from the fact that there is, in several

places, no intensity or texture difference between the

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

404

50 100 150 200 250

100

150

200

250

mesorectum

femoral head

(hip bone)

colorectum

coccyx

bladder

Figure 1: A slice of an MR colorectal image: off-plane axial

view.

mesorectal fascia and surrounding tissues along the

thin edges. The shape of the mesorectal fascia varies

from patient to patient, as do the slice locations of the

patient.

Subsection 2.1 presents how we characterise

lymph nodes, while subsection 2.2 presents delin-

eation of the mesorectum and lymph nodes using level

set methods. The criteria on an automatic lookup of

lymph candidates are discussed in 2.3.

2.1 Characterisation of Lymph Nodes

As explained earlier, we try to extract implicit prop-

erties of lymph nodes that can be read from images.

The most common way of extracting a property of

image could be its intensity. We have examined sim-

ple statistics of the intensity of the mesorectum such

as moments: mean, variance, skewness and Kurtosis.

We found that such statistics are inadequate to differ-

entiate lymph nodes from the mesorectum.

For this reason, we ﬁrst learn the intensity prob-

ability distribution function (PDF) of the mesorec-

tal fascia. This PDF is likely to be Gaussian as has

been frequently noted, for example (Cremers, 2006).

We have found that the PDF closely approximates a

Gaussian distribution N(µ,σ

). We also tested the

Rayleigh distribution (since this is ideally the noise

model for MRI), but we found that it was not as good

a ﬁt to the intensity histogram as a Gaussian. The pa-

rameters µ and σ

of the Gaussian distribution vary

from patient to patient, so they should be estimated

for each individual.

We model the PDF with

P (x) ≈ α exp{−

(x −µ)

2σ

} (1)

where exp{·} is an exponential function. This re-

quires the ﬁtting of the three parameters

α, µ and

σ to the intensity histogram. The candidate lymph

nodes can be differentiated from the mesorectal fas-

cia using the PDF. We know a priori that the lymph

nodes are darker than fat voxels in the mesorectal fas-

cia and so their intensity will fall into a range on the

left tail of the Gaussian curve. We have found in

our experiments that the lymph nodes are clustered

around, or below, µ −2σ of the estimated Gaussian

curve in equation (1). That is, our criterion for ﬁnding

a candidate lymph node is that it should be an outlier

of the Gaussan estimated with equation (1).

To utilise this characterisation method, we need

to segment the boundaries of the mesorectum, as dis-

cussed in the next subsection.

2.2 Detection of Lymph Nodes

As shown in Figure 1, the edges of the inner con-

tour of the mesorectum, i.e., the boundary of the col-

orectum, is rather clear. However, the outer bound-

ary of the mesorectum, the mesorectal fascia is very

ambiguous. We have tested various methods to seg-

ment the the mesorectal fascia, such as the EM algo-

rithm, hidden Markov random measure ﬁelds (Mar-

roquin et al., 2003), phase congruency (Felsberg and

Sommer, 2001), and anisotropic diffusion (Perona

and Malik, 1990). All these methods have performed

well on other images such as brain images. How-

ever, they performed considerably less well on our

colorectal images, partly because of the poor signal

to noise and partly because of ambiguous boundaries.

We needed a method to segment extremely thin edges

and have found that level set methods had superior

performance to all the other methods of segmentation.

Level set methods are brieﬂy introduced next.

2.2.1 Level Set Methods for Segmentation

There have been numerous papers on segmentation

using the level set methods in image analysis areas

The scale factor α is remained in our expression to ap-

proximate the PDF P since we do not normalise the Gaus-

sian, say G(α,µ,σ). When normalised (i.e. the area below

the Gaussian curve in the upper half plane is 1), it can be

expressed by two parameters G(µ,σ) with the scale factor

√

2πσ

. One can use any of the both expressions for op-

timisation. In order to ﬁnd optimal parameters, we used

line search, which is based on the gradient of G(α,µ, σ),

and the partial derivatives

∂G

∂α

∂G

∂µ

and

∂G

∂σ

are computed.

Since the function G(α,µ,σ) is linear with respect to α and

σ appears only in exp(·), this separation of the scale fac-

tor makes

∂G(α,µ,σ)

∂σ

simpler than

∂G(µ,σ)

∂σ

which tends to be

easily perturbed by a small change of σ and optimisation is

prone to fail with.

CHARACTERISATION AND AUTOMATIC DETECTION OF LYMPH NODES ON MR COLORECTAL IMAGES

405

since the early work in the 90’s such as Malladi et al.

(Malladi et al., 1995) and Caselles et al. (Caselles

et al., 1997). One of the well known advantages of

such methods is that they are able to model topolog-

ical changes of boundaries of objects, merging and

splitting, during the evolution of an embedded func-

tion (Sethian, 1999).

Within the level set framework of Osher and

Sethian (Osher and Sethian, 1988), various types of

energy functionals are employed (Chan and Vese,

2001; Paragios and Deriche, 2000; Yezzi and Soatto,

2003; Li et al., 2005). Most researchers use a signed

distance function for an initial φ; but recently some

have suggested that the initial function does not have

to be a distance function. With alternative types of

initial functions such as piecewise constant functions,

Lee and Seo (Lee and Seo, 2006) and the work of D.

Cremers’s group have demonstrated successful imple-

mentations of level set methods.

We adopted the model proposed by Chan and Vese

(Chan and Vese, 2001), since it has shown superior

performance on our colorectal images to other level

set models. The model is not based on the image gra-

dient of the image to stop the process, but is based on

the segmentation techniques of Mumford-Shah func-

tional (Mumford and Shah, 1989). The ﬁtting term

of the their energy functional is designed to be min-

imised when the segmented contour is on the bound-

ary of the object; the regualisation term is deﬁned

by the area of the region inside the segmented con-

tour and its length. The model does not need to

smooth the raw image for segmentation, which is re-

quired in many other segmentation methods. When

smoothed, the very thin edges on our data sets were

buried into the surrounding tissues and this resulted in

poor performance of segmentation. The model gener-

ally copes well with the objects of ambiguous bound-

aries or reasonably noisy boundaries on images.

2.2.2 Segmentation of the Mesorectal Fascia

Chan and Vese (Chan and Vese, 2001) assume that the

intensity of an image can be approximated by a piece-

wise constant function u :

Ω → R as do many other

segmentation techniques. In a simple case, a grey im-

age can be represented by a binary image, having two

values of intensity, and the locations (x,y) ∈ Ω where

there is a jump in the value of u occur will give a

segmentation result, a contour as the boundary of an

object.

A contour C ⊂ Ω is implicitly represented by the

zero level set of a Lipschitz function φ : Ω → R

in the level set method (Osher and Sethian, 1988);

C = {(x,y) ∈ Ω : φ(x,y) = 0}. The optimal contour

of the segmentation is attained by the evolution of the

embedding function φ(x,y,t) at time t and the pro-

cess amounts to solving a partial differential equation

(PDE) in the level set method;

∂φ

∂t

= 0.

Chan and Vese model an image as u(x,y) =

H(φ(x,y)) + c

{1 −H(φ(x,y))}, (x,y) ∈

Ω, where

and c

are constants, and H is the Heaviside func-

tion. The constants c

and c

are in fact the average

values of intensity where φ ≥ 0 and φ < 0, respec-

tively.

To ﬁnd an optimal piecewise constant function

to approximate the image, they evolve an embed-

ding function φ minimising the energy functional

F (c

,φ) for appropriate parameters µ ≥ 0, λ

> 0

and λ

> 0;

F = µ

Ω

δ(φ(x,y))|∇φ(x,y)|dxdy (2)

+ λ

Ω

(x,y) −c

H(φ(x,y))dxdy

+ λ

Ω

(x,y) −c

{1 −H(φ(x,y))}dxdy,

where δ is the 1D Dirac measure (as the weak deriva-

tive of H). The functional is a particular case of the

Mumford-Shah minimal partition problem (Mumford

and Shah, 1989). In order to solve the minimization

problem using variational calculus, we used the ap-

proximation H

∈C

(

Ω) of the Heaviside function H

and and its derivative δ

in the energy function F as in

(Chan and Vese, 2001): H

(z) =

(1 +

arctan(

)),

(z) =

π(ε

)

. Note that lim

ε→0

= H. The Euler-

Lagrange equation associated to the approximation to

equation (2) is led to

∂φ

∂t

= 0 in (0,∞) ×Ω, (3)

(IC) φ(0, x,y) = φ

(x,y) in Ω,

(BC)

(φ)

|∇φ|

∂φ

∂~n

= 0 on ∂Ω,

where ~n denotes the exterior normal to the boundary

∂Ω, and ∂φ/∂~n denotes the normal derivative of φ at

the boundary. The partial derivative in the PDE (3) is

∂φ

∂t

= δ

(φ)

µdiv(

∇φ

|∇φ|

) −λ

(u −c

)

+ λ

(u −c

)

The level set model with ε = 1 is used to segment the

mesorectum as well as candidates of lymph node.

2.3 Automatic 3D Lookup from 2D

Data Set

If the proposed methods are fully automatic, they

would be more practical. This subsection discusses

implementation criteria of automatic lookup to make

our methods fully automatic.

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

406

The proposed methods of lymph node analysis can

be implemented with three stages: segmentation us-

ing a level set method, a lookup of candidates; analy-

sis of the candidates with a Gaussian estimate.

The level set method described in the previous

subsection is also used for delineation of lymph can-

didates on an image. The zero level set results in nu-

merous blobs on the image, and from them we must

select candidates of lymph nodes to analyse. The level

set implementation and Gaussian ﬁtting proposed in

this section can be automatic. If we can make the

selection of candidates from the zero level set auto-

matic, the whole process will be fully automatic.

In section 2.1 we suggested intensity as a classi-

ﬁcation criterion of lymph nodes where Gaussian ﬁt-

ting is utilised. There are other classiﬁcation criteria

to be considered for lymph nodes and they will be in-

cluded in our automatic lookup stage.

In the clinical studies of lymph nodes (Brown

et al., 2003; Lee et al., 1984), large lymph nodes are

a sign of malignancy; 60% of involved lymph nodes

are 4mm diameter or larger in 2D images. We try to

look up even smaller blobs than this cilincal criterion

of size, which is based on human vision.

We look for candidates, 3D blobs, by reading 2D

images. Therefore, we need to consider connectivity

of 2D blobs between slices. For the connectivity, we

consider geometric structure of anatomy appeared on

2D image slices and provide a criterion of distance. If

a dark blob extends over a consecutive slice of thick-

ness 3mm and it runs at an extreme slope through

slices, it could be located as far as 8 pixel distance

on the consecutive slice of a 0.39mm ×0.39mm pixel

size; 4 pixel distance for a 0.78mm ×0.78mm pixel

size. Since patients remain still during an MR scan

and there is very little motion due to breathing, we

do not need to consider motion effect in the images.

Therefore, this geometric criterion is reasonable.

In the next section, the models summarised in this

section are implemented on MR images.

3 EXPERIMENTS

The images used in our experiments are T

weighted

MR colorectal images stored in DICOM format. They

are scanned off-plane and oblique axial thin section

as shown in Figure 1. Angulation of a scan plane per-

pendicular to the rectum is known in general to give

better information than other standard image planes

such as coronal and sagittal since the rectum is at an

angle. All images tested in this paper were scanned

either at John Radcliffe Hospital or Churchill Hospi-

tal, Oxford, UK. The parameters used in the 1.5T MR

scanner were TE = 120ms, TR = 6500ms, ﬂip angle

= 90

◦

. Each slice of a sequence is taken at a uniform

distance.

Due to strong edges of neighbouring organs in

contrast to extremely thin edges of the mesorectal fas-

cia, we crop the images as in Figure 1 to a smaller

ﬁeld of view than those shown in the left of Figure 2.

For our purposes, lymph node detection and char-

acterisation, we need to segment three objects from

images: the mesorectum, colorectum and a lymph

node candidate scattered in the mesorectum. Among

the various level set models that we have tested, such

as edge stopping, geodesic, and bandwidth, the model

of (Chan and Vese, 2001) showed better performance

on our images than other models. Their 2D model has

been found to be better than its 3D generalisation or

multi-phase model for our images.

slice 3

20 40 60 80 100 120

100

120

20 40 60 80 100 120

100

120

Figure 2: A small ﬁeld of view MR slice(left); its segmen-

tation results of a level set method (right).

Subsection 3.1 presents the detection and charac-

terisation of a single candidate of lymph node. The

methods are extended to the automatic detection of

all possible candidates in subsection 3.2; a validation

issue is discussed in subsection 3.3.

3.1 A Single Candidate of Lymph Node

The ﬁrst example set is presented in the left column

of Figure 3. The data set is a sequence of consec-

utive slices of 3mm thickness and the pixel size is

0.78mm ×0.78mm.

As described in section 2, we employed the model

of Chan and Vese and the resulting zero level set of an

optimal solution of the differential equation (3) was

obtained as presented in Figure 2 (right).

Then we organise the zero level set and selected

the contours delineating the three objects as presented

in the right column of Figure 3 at each corresponding

row. In the ﬁgures in the right column, the mesorec-

tum is delineated as the region enclosed by two closed

contours (red) for each of three images; the inner con-

tour is the boundary of the colorectum and the outer

contour the mesorectal fascia. A lymph node candi-

date is delineated by the green contour in the second

CHARACTERISATION AND AUTOMATIC DETECTION OF LYMPH NODES ON MR COLORECTAL IMAGES

407

and third ﬁgure. In this case, as a result of our method,

an individual lymph node candidate is examined.

slice 1

20 40 60 80 100 120

100

120

slice1: 14 iterations

20 40 60 80 100 120

100

120

slice 2

20 40 60 80 100 120

100

120

slice2: 18 iterations

20 40 60 80 100 120

100

120

slice 3

20 40 60 80 100 120

100

120

slice3: 68 iterations

20 40 60 80 100 120

100

120

Figure 3: A set of consecutive MR slices with a small ﬁeld

of view (left column); their corresponding mesorectal con-

tours selected from segmentation results by level set (right

column). Mesorectal contours and colorectal contours are

red, and a lymph node candidate green.

Our segmentation results enabled us to build a

PDF and to use it detect lymph nodes as outliers, as

described in section 2. We collect the intensity values

of the mesorectal fascia of the MR slices to learn its

voxel PDF. The voxel intensity histogram is depicted

in Figure 4 (blue stem and head). The optimal param-

eters of the Gaussian distribution are µ = 190.986 and

σ = 40.924. The optimal ﬁt, with these parameters is

depicted by a red curve in Figure 4. Two vertical lines

for the values of µ −σ (solid line) and µ −2σ (dotted

line) on the horizontal axis are also plotted in the ﬁg-

ure for the purpose of demonstration of our criterion.

Now we collect the intensity values of the lymph

candidate whose planar sections are depicted by green

contours in Figure 3. The histogram of the lymph

candidate (pink heads) is overlaid on the histogram of

the voxel intensity of the mesorectum, which appears

around the µ −2σ, at the left tail of the red Gaussian

curve in Figure 4. The mean intensity of the lymph

candidate is computed. The value is 118.709 and is

slightly larger than µ −2σ. These values are sum-

marised in Table 1 for comparison.

We have also tested datasets at a higher resolution,

i.e. images about four times bigger than the dataset

0 100 200 300 400 500

100

150

200

250

300

350

400

lymph candidate intensity and Gaussian fitting of MF intensity (3 slices)

intensity of MF (3 slices)

approximate Gaussian

µ − σ

µ − 2σ

lymph candidate

Figure 4: The histogram of the intensity of the mesorectum

of the three slices, delineated in Figure 3 (blue stem) and

its Gaussian estimate (red curve). The histogram of the in-

tensity of a lymph node candidate is overlaid (pink heads),

which is clustered around µ −2σ.

Table 1: Comparison of lymph node candidate to the pa-

rameters of Gaussian in Figure. 4

mean (lymph

candidate)

µ −2σ µ −1.5σ µ −σ

118.709 109.138 129.600 150.062

presented in Figure 3. The slice thickness of this ex-

ample set is 3mm and pixel size is 0.39mm ×0.39mm.

The four images are in Figure 5. The selected con-

tours from the segmentation results using the level set

method are presented in the ﬁgure.

slice1

50 100 150 200

100

120

140

160

180

200

220

slice2

50 100 150 200

100

120

140

160

180

200

220

slice3

50 100 150 200

100

120

140

160

180

200

220

slice4

50 100 150 200

100

120

140

160

180

200

220

Figure 5: A set of consecutive MR slices with a small ﬁeld

of view and their mesorectal contours selected from seg-

mentation results by level set. Mesorectal contours and col-

orectal contours are red, and a lymph node candidate green.

The intensity of the segmented mesorectum of the

dataset is approximated by a Gaussian distribution in

Figure 6; the estimated parameters are µ = 336.906

and σ = 45.488. For this example, the mean intensity

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

408

0 100 200 300 400 500 600 700

200

400

600

800

1000

1200

1400

1600

1800

lymph candidate intensity and Gaussian fitting of MF intensity (4 slices)

intensity of MF (4 slices)

approximate Gaussian

µ − σ

µ − 2σ

lymph candidate

Figure 6: The histogram of the intensity of the mesorectum

of the four slices, segmented in Figure 5 (blue stem) and its

Gaussian estimate (red curve). The histogram of the inten-

sity of a lymph candidate is overlaid (pink heads), which is

clustered below µ −2σ at the left tail of the Gaussian.

of the lymph node candidate seen in Figure 5 is com-

puted as 206.245. The comparison of the intensity

of the lymph node to the intensity of the mesorectal

fascia is given in Table 2. In this dataset, the mean

intensity of the lymph candidate is far smaller than

µ −2σ.

Table 2: Intensity comparison of lymph node candidate to

the parameters of Gaussian in Figure 6.

mean (lymph

candidate)

µ −2σ µ −σ

206.245 245.929 291.417

If we deﬁne the criterion of lymph node to be

smaller than µ −1.5σ, then the two individual can-

didates tested in this subsection are outliers of Gaus-

sians and are classiﬁed as lymph nodes.

3.2 Multiple Candidates of Lymph Node

We extend the methods for a single candidate pre-

sented in subsection 3.1 to all possible candidates.

In the ﬁrst example set in subsection 3.1, we or-

ganised the zero level set to select possible candi-

dates from a few dozens to a hundred small blobs per

image. The candidates which are located within the

mesorectum and not very tiny are selected. Consider-

ing the criteria discussed in subsection 2.3, we set the

thresholds of the size of possible candidates and of

the distance for connectivity over slices. Our aim is

to provide more scientiﬁc methods and try to capture

small lymph nodes which can be missed by human vi-

sion. Hence, we select all blobs of 3mm diameter (i.e.

11 pixels of size 0.78mm×0.78mm) or larger and they

are candidates. The candidates are then analised with

their Gaussian estimates individually, as done in sec-

tion 3.1.

1st blob on slice 1

20 40 60 80 100 120

100

120

2nd blob on slice 1

20 40 60 80 100 120

100

120

Figure 7: Single blobs found as candidates (green).

In this example of three slices, we selected 14 can-

didates from our lookup and some of them are pre-

sented in Figure 5 (appeared on consecutive slices in

the right column) and Figure 7 (appeared on a sin-

gle slice). The intensity of each candidate is grouped

and its mean intensity is computed. The mean inten-

sity values of these 14 candidates are plotted in the

horizontal bar chart in Figure 8. In the ﬁgure, seven

candidates are outliers of the Gaussian distribution in

Figure 4 and estimated as lymph nodes as suggested

in section 2. Some of the seven lymph nodes are de-

picted in Figure 9.

0 20 40 60 80 100 120 140 160 180

mean intensity of 14 lymph candidates

lymph candidates

intensity

µ − 2σ

µ −1.5σ

Figure 8: The mean intensity of detected candidates of the

three slices in Figure 3 are collected and their mean inten-

sity is plotted in the bar chart. Only seven candidates are

below µ −1.5σ of the Gaussian (solid vertical lines).

For the second example set in section 3.1, the

same procedure is carried out. In this example of four

slices, we selected ﬁve candidates; some of them are

shown in Figure 10 as well as Figure 5. The mean

intensity values of these ﬁve candidates are plotted in

the horizontal bar chart in Figure 11; all the candi-

dates are outliers of the Gaussian distribution in Fig-

ure 6 and estimated as lymph nodes.

We also tested a number of examples in which we

performed manual segmentation, In all these experi-

ments (of both automatic and manual segmentation),

CHARACTERISATION AND AUTOMATIC DETECTION OF LYMPH NODES ON MR COLORECTAL IMAGES

409

identified lymph node 1

20 40 60 80 100 120

100

120

identified lymph node 5

20 40 60 80 100 120

100

120

identified lymph node 6

20 40 60 80 100 120

100

120

identified lymph node 7

20 40 60 80 100 120

100

120

Figure 9: Candidates classiﬁed as lymph nodes (green).

4−th blob on slice 3

50 100 150 200

100

150

200

connected to 4−th blob on slice 4

50 100 150 200

100

150

200

Figure 10: Blobs selected as a candidate (green); connected

to consecutive slices.

0 50 100 150 200 250 300

mean intensities of 5 lymph candidates

lymph candidates

intensity

µ−2σ

µ−σ

Figure 11: The mean intensity of detected candidates of the

four slices in Figure 5 are collected and their mean intensity

is plotted in the bar chart. they are all below µ −2σ of the

Gaussian (dotted vertical lines).

the mean values of intensity of lymph candidates are

smaller than or slightly larger than µ −2σ. These ex-

periments enable us to ascertain that a mean value of

intensity is smaller than µ −2σ but for a population

set of a small size we need allow some margin of er-

ror in this criterion. For this reason, we suggest that

candidate lymph nodes correspond to being outliers

of the estimated Gaussian as

mean(lymph intensity) < µ −1.5σ

The estimation of the Gaussian of the mesorec-

tum enabled us to provide an explicit criterion for the

candidate to be classed as a lymph node. The whole

process of the proposed methods is fully automatic.

3.3 Manual Segmentation of Lymph

Candidates

We have compared our automatic method to manual

segmentation for the example set in Figure 9. We de-

tected ﬁve lymph nodes from our manually segmented

candidates using the same criteria as in the automatic

segmentation. Among the seven blobs classiﬁed as

lymph nodes with automatic segmentation, four were

also detected with manual segmentation. Note that

this disagreement is not only due to the quality of

automatic segmentation but due to the subjectivity of

segmenting small objects.

An example enlarged is presented in Figure 12

which shows the difﬁculty of manual segmentation of

a small object on a noisy image.

lymph − manual segmentation

42 44 46 48 50 52 54 56 58 60

100

102

104

106

Figure 12: A manually segmented lymph candidate on a

blurred image.

Another type of problems are demonstrated in

Figure 13; the ﬁgure on the left hand side was auto-

matically segmented, and the ﬁgure on the right hand

side was manually segmented. The ﬁgure demon-

strates the difference between machine vision (left)

vs. human vision (right) to segment lymph candi-

dates. The bump pointed by a broken arrow was

segmented into the region of colorectum with auto-

matic segmentation (left), but it was segmented man-

ually as a dark blob near the boundary of the col-

orectum (right). The other two dark blobs pointed by

solid arrows (left) were segmented automatically and

were included in the set of our candidates; while they

were merged into one object with manual segmenta-

tion (right), which can be classiﬁed as blood vessel

rather than a lymph node candidate. These are the

same problems as the current methods practiced by

clinicians, heavily relying on human vision. There-

fore, at this stage without further evidence, it is difﬁ-

cult to say if the three lymph nodes additionally de-

VISAPP 2008 - International Conference on Computer Vision Theory and Applications

410

tected by automatic segmentation and our charateri-

sation criterion are indeed false positives.

automatic segmentation

100 110 120 130 140 150 160 170 180

110

120

130

140

150

160

170

180

190

manual segmentation of lymph candidates

100 110 120 130 140 150 160 170 180

110

120

130

140

150

160

170

180

190

Figure 13: Differences between machine vision (left) vs.

human vision (right) in segmenting lymph node candidates.

Broken arrows: Part of the colorectum (left) or a blob

(right)? Solid arrows: two blobs (left) or blood vessel

(right)?

4 CONCLUDING REMARKS AND

DISCUSSIONS

We have proposed methods to characterise and detect

lymph nodes in MR colorectal images. Using a level

set method, we were able to delineate the boundaries

of the mesorctal fascia, colorectum, and a lymph node

candidate from non-trivial images. With the delin-

eated the boundaries, we estimated the intensity PDF

of the mesorectum, and approximated it by a Gaussian

distribution. The intensity of the segmented lymph

node candidate was then compared to the Gaussian

distribution. We suggest that lymph nodes can be de-

tected as outliers of the estimated Gaussian, with a

mean intensity smaller than µ −1.5σ. This provides a

more scientiﬁc means of lymph node characterisation.

The implementation is fully automated.

The number, size, and location of lymph node can-

didates on each slice vary. In our data sets, the thick-

ness of slice (usually from 3mm to 5mm) is quite large

in comparison to the pixel size (0.78mm ×0.78mm or

0.39mm×0.39mm). We cannot capture the true infor-

mation between slices and this may keep from precise

detection using information on images. For example,

learning the 3D connectivity of lymph candidates be-

tween 2D slices is not straightforward unless they are

sufﬁciently big. Since we implement a region merg-

ing algorithm individually for 2D slices and the slice

thickness is often larger than the lymph node size, a

high possibility of missing information exists. How-

ever, for small dark blobs that are prone to be missed

by human vision are able to be detected by the pro-

posed methods.

As discussed in subsection 3.3, validation is not

straightforward to carry out. Considering that pathol-

ogists routinely ﬁnd more nodes in the dissected spec-

imen than reported by radiologists, a true validation of

a lymph node detection is very difﬁcult.

Though better than the other segmentation meth-

ods examined, our level set implementation was not

always successful; leaking occurred when edges were

extremely thin and strong edges were nearby, as seen

in some slices in section 3.1 ﬁgures. Our character-

isation criterion of lymph nodes (Gaussian distribu-

tion thresholding) might not be sufﬁcient. We think

our work is an initial study for more scientiﬁc meth-

ods of cancer staging and hope it promotes the active

study of lymph node detection using image analysis

approaches.

Some clinicians use signal heterogeneity as a

lymph characteristic. In our data sets, this was not

the case and thus we did not consider it in this pa-

per. However, for a large study population, this could

be taken into account in combination with the pro-

posed methods. Another plausible, yet highly in-

volved method may be to analyse the signal change

and heterogeneity of a lymph node where contrast

agent is used. These could be the subjects of our on-

going work.

ACKNOWLEDGEMENTS

This work was supported by GE.

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