Automatic Detection of Distal Humerus Features: First Steps

Jos

e Negrillo-C

ardenas, Juan-Roberto Jim

enez-P

erez and Francisco R. Feito

Graphics and Geomatics Group of Ja

en, University of Ja

en, Campus Las Lagunillas S/N, Ja

en, Spain

Keywords:

Bone, Humerus, Feature Detection, Landmark Detection, Geometrical Approach, Spatial Decomposition.

Abstract:

Identiﬁcation of speciﬁc landmarks in tissues is fundamental for understanding the human anatomy in medical

applications. Speciﬁcally, the assessment of bone features allows to detect several pathologies in orthopedics.

The recognition has been formerly carried out via visual identiﬁcation, providing insufﬁcient accuracy.

Automatic solutions are required to improve the precision and minimize diagnostic and surgical planning

time. In this paper, we study distal humerus landmarks and a new algorithm to automatically detect them in

a reasonable time. Our technique does not require a prior training, as a geometrical approach with a spatial

decomposition is used to explore several regions of interest of the bone. Finally, a set of experiments are

performed, showing promising results.

1 INTRODUCTION

Improving the detection of speciﬁc tissues,

pathologies, illnesses, etc. is one of the main goals

of the fruitful relationship between medicine and

computer graphics research ﬁelds (Vidal et al., 2006).

Medical images represent a classic and effective

instrument to advance in the achievement of this goal.

The identiﬁcation of bone features is relevant to a

better comprehension of the musculo-skeletal system,

to diagnose some diseases, to plan a bone fracture

reduction or to design patient speciﬁc implants

(Brownhill et al., 2007; Ron et al., 2002). In the

special case of a joint, the right alignment between

bones guarantees an adequate degree of freedom to

move. As a result, identifying the corresponding

3D anatomical landmarks in each bone is the main

target. A visual identiﬁcation of these characteristics

is not precise enough, as Brownhill et al. (2006)

demonstrate in the case of the elbow. Consequently,

computer guided or fully automatic approaches are

required.

In this work in progress we present an automatic

detection of humeral landmarks using a geometrical

approach and spatial decomposition. Our proposal

requires no user interaction from a mesh model of

a humerus. We design a technique for each of the

deﬁned landmarks.

The paper is organized as follows: Section 2

overviews related work regarding automatic detection

of landmarks with special focus on joints. Section 3

describes the anatomy of humerus. Section 4 details

our proposal. Section 5 shows preliminary results.

Finally, in section 6 we detail future work and our

conclusions.

2 PREVIOUS WORK

This review focuses on automatic detection of

landmarks. Landmark detection techniques have been

used throughout the study of different pathologies

in general medicine. Barbu et al. (2010) evaluate

the lymph node size as an indicator of the evolution

of a cancer treatment. Artiﬁcial intelligence can

label organs in an automatic way using machine

learning techniques. Han et al. (2015) classify several

brain regions using multi-resolution regression. Kim

et al. (2017) propose a supervised machine learning

approach to automatically localize the left ventricle

center point and the anterior right ventricle insertion

point. Yefeng Zheng et al. (2012) present a

discriminative learning algorithm to segment eight

aortic valve landmarks from a C-arm computed

tomography. In orthopedics, Yang et al. (2015)

applied convolutional neural networks and Baek

et al. (2013) statistical models to predict femoral

landmarks.

Geometrical approaches give also accurate results

without any kind of prediction. In general, they are

more computer-demanding, but a previous training

stage becomes unnecessary. A simple spatial

354

Negrillo-Cárdenas, J., Jiménez-Pérez, J. and Feito, F.

Automatic Detection of Distal Humerus Features: First Steps.

DOI: 10.5220/0007686103540359

In Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2019), pages 354-359

ISBN: 978-989-758-354-4

CapitulumTrochlea

Lateral

Epicondyle

Medial

Epicondyle

Humeral

Shaft Axis

(HSA)

Epicondylar

Axis (ECA)

Flexion-Extension

Axis (FEA)

Figure 1: A visual representation of the most representative

landmarks in a healthy distal humerus.

decomposition can be used to efﬁciently detect

landmarks in speciﬁc bones. Subburaj et al.

(2009) detect tibial features by curvature and spatial

adjacency assessment. McDonald et al. (2009)

determined a set of features in humerus to underpin

an adequate alignment of the elbow joint. However,

they provide insufﬁcient technical details about how

landmarks were obtained.

3 ANATOMY OF THE DISTAL

HUMERUS

The humerus is a bone situated in the arm that

connects shoulder and elbow. It is divided into three

parts. The humeral head is the rounded upper region

that allocates the shoulder. A long, cylindrical body

determines the arm direction. The most distal part

determines the elbow joint, that is composed of the

humeroulnar and humeroradial joints. They allow the

arm to be extended from 0 to 180

in a healthy person

(Delgado Mart

ınez, 2015).

Distal humeral width is commonly deﬁned as

the distance between medial and lateral epicondyles

(also known as epicondylar distance). Finally, the

lowest part of the humerus contains several hollows,

adapting it to ulna and radius. Figure 1 shows several

points of interest: trochlea, capitulum, coronoid

and olecranon. We refer the reader to An (2018);

Shiba et al. (1988); London (1981) for further details

regarding anatomy and biomechanical behavior of the

elbow.

Apart from the referred anatomical features,

several geometric landmarks can be established in

order to improve diagnosis and to compare between

other humeri. According to McDonald et al. (2009),

humeral shaft axis (HSA) is deﬁned as a line along

the humerus, representing the geometric centers of

distal and proximal endosteal canals. Epicondylar

axis (ECA) is also designated as the line through

medial and lateral epicondyles. An orthonormal

reference coordinate system is deﬁned with them,

being the direction of HSA as up vector. The cross

product of HSA and ECA represent f ront and up ×

f ront determines the right orientation of the humerus.

Note that right is the re-orthonormalization of ECA.

Finally, the ﬂexion-extension axis (FEA) is calculated

to represent the direction of the elbow joint.

4 DETECTION OF LANDMARKS

Automatic detection of features is critical to

signiﬁcantly save processing time. Thus, manual

placement becomes unnecessary to correctly situate

the landmarks.

Our technique recognizes anatomical landmarks

by using a geometric approach with spatial

decomposition. Initially, a triangle mesh is generated

from CT images using a classical Marching cubes

algorithm (Lorensen and Cline, 1987). Afterwards

the resulting mesh is simpliﬁed to reduce geometry

Figure 2: Reference coordinate axes of a humerus with its

oriented bounding box.

Automatic Detection of Distal Humerus Features: First Steps

355

a b c

Figure 3: Steps to detect the center of a capitulum: (a) iterative cutting, (b) point cloud of the spherical cap, (c) approximated

sphere (bone opacity is reduced to improve visualization).

a b c

Capitulum center

Figure 4: Steps to detect the center of the trochlea: (a) an iterative cutting of a slice containing the capitulum center, (b) new

cutting plane through local minimum of the medial zone, (c) approximated circle center.

using a decimation ﬁlter. In addition, a Laplacian

smoothing ﬁlter is applied in order to reduce

irregularities of the resulting mesh due to the noise of

initial medical images.

A detection of the initial orientation is necessary

to explore the spatial structure of the bone. By

employing a geometrical approach, we consider the

direction of a humerus as the longest side of its

oriented bounding box (OBB). In this manner, the

middle-sized edge corresponds to width, and the

shortest, to depth of the 3D model. Geometric

operations are performed using the OBB edge

directions as a reference coordinate system. Then,

x, y, z vectors deﬁne the orientation of the bone (see

Figure 2).

As mentioned before, humeral shaft axis

represents a line along the proximal endosteal canal

of the humerus. This axis is obtained by intersecting

the upper humerus to a set of manually determined

planes parallel to the bottom of the OBB. Finally,

HSA is deﬁned as a linear approximation of the mass

centers of each slice, applying three-dimensional

orthogonal regression (Schneider and Eberly, 2003).

Medial and lateral epicondyles are computed

as tangent points of both ﬂanking planes of the

OBB, i.e., those parallel to the yz face as it is

shown in Figure 2. However, the use of irregular

meshes provoked by low-quality medical images

may cause unpredictable behaviour and void results.

Epsilon-based operations overcome this problem by

adding an inner offset to the original planes. Finally,

epicondylar axis is deﬁned by connecting both

detected points.

The calculation of HSA and ECA is thus resolved

as it has been commented previously. However,

detection of other regions of interest is not fully

concluded in an automated way. McDonald et al.

(2009) also studied the detection of capitulum and

trochlea by using sphere and circle ﬁt, respectively,

in predeﬁned areas. They do not provide sufﬁcient

details about the method for establishing the zones.

Accordingly, in this work in progress, we identiﬁed

without any kind of interaction the centers of trochlea

and capitulum, by splitting distal humerus with a set

of planes.

Anatomy of distal humerus shows the capitulum

as a sphere at the end of the bone (Kawanishi et al.,

2013). An iterative cutting is required to ﬁnd a

spherical cap. The process is stopped when two

polygons are encountered at least. The mesh above

the plane represents a point cloud in where a sphere

can be ﬁtted using a least squares method (Eberly,

2000). As a result, the origin of the sphere is an

accurate approximation of the capitulum center, as it

GRAPP 2019 - 14th International Conference on Computer Graphics Theory and Applications

356

Figure 5: Landmarks detected in four humeri. Results are visually accurate.

is shown in Figure 3.

Trochlear sulcus is centered in distal humerus,

next to the capitulum. It contains a groove to adapt

the humerus to both ulna and radius. The center

of the trochlea is placed approximately at the same

height as the center of the capitulum. Thus, a

cutting plane containing it, parallel to the bottom

of the OBB, is deﬁned. The clip function results a

polygon representing the contour of the slice (Figure

4.a). Due to the trochlear and olecranon sulcus, a

narrowing zone contains the center of the trochlea.

The desired zone can be detected by minimizing the

medial section of this polygon. Finally, an orthogonal

plane to this minimum section is deﬁned to slice the

most distal part of the humerus (Figure 4.b). The

least squares circle ﬁtting algorithm (Eberly, 2000) is

applied to the resulting strip to detect the requested

point (see Figure 4.c).

To conclude, the ﬂexion-extension axis (FEA) is

deﬁned by the connection of trochlea and capitulum

centers.

5 EXPERIMENTS AND RESULTS

This section details the experiments performed to

determine the validity of our method.

A set of four phantom humeri were scanned using

a structured light 3D scanner Artec Eva

. Distal

part of each one was registered and 3D meshes were

generated using Artec Studio Professional software.

A program based on The Visualization Toolkit,

VTK (Schroeder et al., 2006) was used to implement

and test our algorithm. Currently, we calculated the

following points of interest for each bone:

• Trochlea center.

https://www.artec3d.com/portable-3d-scanners/artec-eva

• Capitulum center.

• Lateral epicondyle.

• Medial epicondyle.

• Humeral shaft axis.

• Epicondylar axis.

• Flexion-extension axis.

Currently, results are visually satisfactory (Figure 5),

as they match with human atlas of anatomy (Rohen

et al., 2011). Further testing is required to compare

between results and ensure a robust algorithm in

different situations.

5.1 Current Issues

As mentioned in Section 4, a correct initial orientation

of the bone is critical to properly apply a spatial

decomposition.

VTK constructs an OBB using a classical

approach (Gottschalk et al., 1996). Mean of the

vertices and a covariance matrix are calculated.

Then, three eigenvectors deﬁne the bounding box

edges. Nevertheless, they depend on initial geometry

and orientation of the mesh, producing invalid

results for our algorithm in some cases. In other

words, the reference corner of the OBB is placed

differently depending on models used. When an

initial orientation and a corner are predeﬁned, our

algorithm runs as expected.

6 CONCLUSIONS

We have described an automatic feature detection

algorithm for distal humeri from surface models. This

approach can be applied to detect some anomalies

in a bone or treat pathologies. Our technique has

Automatic Detection of Distal Humerus Features: First Steps

357

been based on a geometrical approach, using a spatial

decomposition. We have accomplished our primary

objective of obtaining bone landmarks in an efﬁcient

way, requesting no interaction. In this position, main

issues of our algorithm have been also analyzed.

Preliminary experiments show promising results in

terms of accuracy and performance.

Currently, we are improving the robustness of the

algorithm, solving mentioned problems in Section

5.1. For instance, current manual parameters

should be dynamically established for each case.

Further testing using additional models is required

to obtain more detailed results (e.g., accuracy and

CPU/memory usage).

ACKNOWLEDGEMENTS

Authors are part of Graphics and Geomatics Group

of Ja

en (TIC-144). This research was supported

by the Spanish Ministry of Education, Culture and

Sports via a doctoral grant to the ﬁrst author (Ref.

FPU16/01439) and partially by Spanish Ministry of

Science, Innovation and Universities through research

projects DPI2015-65123-R and TIN2017-84968-R.

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