A Novel Use of Hyperspectral Images for Human Brain Cancer

Detection using in-Vivo Samples

Himar Fabelo

, Samuel Ortega

, Raúl Guerra

, Gustavo Callicó

, Adam Szolna

, Juan F. Piñeiro

Miguel Tejedor

, Sebastián López

and Roberto Sarmiento

Institute for Applied Microelectronics, University of Las Palmas de Gran Canaria, Las Palmas de Gran Canaria, Spain

Department of Neurosurgery, University Hospital Doctor Negrín, Las Palmas de Gran Canaria, Spain

Keywords: Brain Cancer Detection, Hyperspectral Imaging, Random Forest, Classification Algorithms.

Abstract: Hyperspectral Imaging is an emerging technology for medical diagnosis issues due to the fact that it is a non-

contact, non-ionizing and non-invasive sensing technique. The work presented in this paper tries to establish

a novel way in the use of hyperspectral images to help neurosurgeons to accurately determine the tumour

boundaries in the process of brain tumour resection, avoiding excessive extraction of healthy tissue and the

accidental leaving of un-resected small tumour tissues. So as to do that, a hyperspectral database of in-vivo

human brain samples has been created and a procedure to label the pixels diagnosed by the pathologists has

been described. A total of 24646 samples from normal and tumour tissues from 13 different patients have

been obtained. A pre-processing chain to homogenize the spectral signatures has been developed, obtaining

3 types of datasets (using different pre-processing chain) in order to determine which one provides the best

classification results using a Random Forest classifier. The experimental results of this supervised

classification algorithm to distinguish between normal and tumour tissues have achieved more than 99% of

accuracy.

1 INTRODUCTION

Malignant brain tumours, with a global incidence

around 3.5 per 100,000 people, are among the most

lethal and challenging cancers for treatment. Surgical

resection is one of the most important pillars in the

treatment of these tumours, but due to their locations,

sometimes arising from very eloquent areas of the

brain, and their diffuse and infiltrating limits, the total

excision is sometimes cumbersome or impossible to

achieve.

Modern Neurosurgery for these tumours relies on

image-guided resection, but it needs expensive and/or

invasive techniques, such as the Neuronavigation,

intraoperatory Magnetic Resonance Imaging (MRI),

injection of reactive for immunofluorescence, etc.

The goal of this investigation is to apply an innovative

and non-invasive technology tool for image-guided

brain tumour resection: Hyperspectral imaging.

This technology is a non-contact, non-ionizing

and non-invasive sensing technique very suitable for

medicine (Lu et al., 2014); (Akbari et al., 2012). It

consists in collecting and processing information

across the electromagnetic spectrum creating a

hyperspectral data-cube with the values of the

reflectance of the light captured in the scene for

different frequencies. This kind of images increases

the amount of information acquired in a scene

compared with the conventional RGB image or a

multispectral image (which has around ten bands), by

capturing data in a large number of contiguous and

narrow spectral bands over a wide spectral range.

Using the information generated by hyperspectral

imaging, it is possible to obtain a spectral signature of

each pixel. This spectral signature allows

differentiating the material or substance that is

presented in the pixel. It is expected that tumours will

be detected as changes in the spectral signatures

compared with normal tissues (Fei et al., 2012);

(Martin et al., 2006).

The work presented in this paper has been

developed within the HELICoiD (HypErspectraL

Imaging Cancer Detection) project. HELICoiD is a

European FET (Future Emerging Technologies)

project that has the aim of discriminating between

normal and tumour tissues in the surface of the human

brain during neurosurgical operations in order to

Fabelo, H., Ortega, S., Guerra, R., Callicó, G., Szolna, A., Piñeiro, J., Tejedor, M., López, S. and Sarmiento, R.

A Novel Use of Hyperspectral Images for Human Brain Cancer Detection using in-Vivo Samples.

DOI: 10.5220/0005849803110320

In Proceedings of the 9th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2016) - Volume 4: BIOSIGNALS, pages 311-320

ISBN: 978-989-758-170-0

 2016 by SCITEPRESS – Science and Technology Publications, Lda. All r ights reserved

311

provide the neurosurgeons with a real-time guide that

can help in the adequate surgical resection. As a

second goal, the project will try to obtain

hyperspectral signatures from different tumours, so it

might give clues to the neurosurgeon about the

tumour histology.

The results of this discrimination process will be

shown to the neurosurgeons by using a false colour

map where tumour and healthy tissues will be clearly

differentiated. This colour map will help them to

accurately determine the tumour boundaries in the

process of brain tumour resection, avoiding excessive

extraction of healthy tissue and the accidental leaving

of small tumour tissues.

2 MATERIALS AND METHODS

This section provides an overview of the

instrumentation and the methodology used to collect

the in-vivo hyperspectral data of human brain

samples.

2.1 Hyperspectral Imaging

Instrumentation

In order to obtain the hyperspectral images of the in-

vivo human brain surface during the neurosurgical

operations, the HELICoiD project has built a

demonstrator capable of simultaneously obtaining

two hyperspectral cubes. The two hyperspectral

cameras selected are the Hyperspec

VNIR A-Series

and the Hyperspec

NIR X-Series, manufactured by

HeadWall Photonics, Massachusetts, USA. The

VNIR (visible and near infrared) camera ranges

between 400 nm to 1000 nm. The NIR (near infrared)

camera ranges between 900 nm to 1700 nm.

Figure 1 shows the main parts of the

demonstrator. The most important elements of the

system are located in the acquisition scanning

platform. Table 1 presents the specifications of the

two push-broom hyperspectral cameras. These

cameras are fixed in a scanning unit composed by a

stepper motor and a screw with a maximum path of

230 mm and a step resolution of 6.17 µm.

Furthermore, a cold light emitter is located together

with the cameras. The cold light emitter is connected

to a 150 W Quartz Tungsten-Halogen system (QTH)

(Figure 2.c), which offers broadband emission in the

VIS (visible) and NIR spectral ranges (400 nm to

2200 nm), through an optical fibre. This system

isolates the high temperatures produced by the

halogen lamp, avoiding a direct emission to the brain

surface.

Data pre-processing system is composed by a

high performance computer which manages the entire

system, especially the acquisition scanning platform

and the interaction with the user through the graphical

user interface (GUI).

Figure 1: HELICoiD demonstrator main parts.

Finally, the processing sub-system platform has

the goal of performing the hyperspectral

classification in order to achieve the results in real-

time. The platform selected for this issue is the Kalray

many-core processor that features MIMD (Multiple

Instruction Multiple Data) architecture (B. D. de

Dinechin et al., 2013). This platform is focused on

intensive computing, low power and embedded

applications.

Table 1: Camera Specifications.

Hyperspec

VNIR

Hyperspec

NIR

Spectral range (nm)

400 – 1000 900 – 1700

Spectral resolution (nm)

2 – 3 5

Slit (μm)

25 25

Spatial bands

1004 320

Spectral bands

826 172

Frame Height (FOV) (mm)

129.21 153.6

Pixel Dimensions (IFOV)

(mm)

0.1287 0.4800

Max Pixels per Frame

1004 320

Max Frames per Capture

1825 489

Dispersion per pixel (nm)

0.74 4.8

Detector array

Silicon CCD InGaAs

Frame rate (fps)

90 100

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Figure 2.b shows the HELICoiD demonstrator

inside the pre-operative area at the University

Hospital Doctor Negrín in Las Palmas de Gran

Canaria, Spain. Figure 2.a presents the acquisition

platform where the cameras and the cold light

element are located. On the left side of the platform,

the VNIR camera is located, and on the right side the

NIR camera is placed. In the middle of the two

cameras, the cold light emitter is located. These three

elements are correctly aligned in order to obtain the

images properly illuminated. Figure 2.d displays the

stepper motor controller, which is in charge of

managing the scanning platform shift.

(a)

(b)

(c)

(d)

Figure 2: (a) Acquisition scanning platform, (b) complete

HELICoiD demonstrator, (c) Quartz Tungsten-Halogen

system and (d) stepper motor controller.

2.2 Hyperspectral Image Dataset

Using the HELICoiD demonstrator, an in-vivo

human brain hyperspectral image database has been

created. The hyperspectral cubes have been obtained

from 13 different patients at the University Hospital

Doctor Negrín. The disease of the tissues captured

during this study involves both primary brain tumours

and secondary tumours (metastasis). All primary

tumours captured have been diagnosed as grade IV

glioblastoma. For secondary tumours two different

types of metastasis, lung and renal, have been

collected.

From this database, a dataset formed by normal

brain tissue and tumour tissue (primary and

secondary) of hyperspectral samples have been

collected and labelled. The work presented in this

paper is only focused in the VNIR hyperspectral

cubes as they have provided better results. Table 2

shows the number of the labelled in-vivo human brain

samples available from the VNIR hyperspectral

cubes.

Table 2: HELICoiD Labelled Spectral Signature Data Base.

Tissue Type Patients # Samples

Normal 9 12604

Tumour

Primary 8 10059

Secondary 4 1983

In order to obtain the samples correctly labelled,

the four steps flowchart presented in Figure 3 has

been followed. First, when the neurosurgeons have

the brain surface exposed, they place two sterilised

rubber ring markers over it. One marker is placed

over the zone where clearly the tumour lesion is

located. The other marker is placed over an area far

from the tumour lesion, where the neurosurgeon can

be quite confident that the brain tissue is healthy.

After that, the operator of the HELICoiD

demonstrator captures the hyperspectral image of this

exposed brain surface.

Figure 3: Data capture and labelling process.

So as to identify the location of the markers over

the brain, the neuronavigator pointer is used. Figure 4

illustrates the use of the neuronavigator to identify the

position of the markers in a MRI.

Next, neurosurgeons remove the tissue inside the

tumour marker. This tissue is sent to the pathologists,

which are the experts who can determine the real

A Novel Use of Hyperspectral Images for Human Brain Cancer Detection using in-Vivo Samples

313

diagnosis of the tissue inside the marker. If the brain

tissue is tumour, pathologists specify the grade and

the type of the tumour.

(a)

(b)

Figure 4: (a) Neuronavigator pointer over the tumour

marker located on the brain surface exposed and (b),

neuronavigator screen capture with the coordinates of the

tumour marker in a MRI.

Finally, with this information, the pixels inside the

markers are cropped manually, avoiding pixels which

could have specular reflections produced by the non-

uniformity of the brain surface. These selected pixels

are labelled and stored with the information provided

by the pathologists. Labelled pixels will be used as

inputs in a supervised classification algorithm

scheme.

Figure 5 presents the most representative bands of

the VNIR hyperspectral image of the patient 12’s

brain surface captured by the demonstrator.

3 CLASSIFICATION SYSTEM

For performing a spectral classification using the

hyperspectral images captured, a classification

system based on a Random Forest (RF) classifier has

been defined. Figure 6 shows an overview of this

classification system.

The first stage of the proposed classification

system is the acquisition step, where the labelled

dataset of the tumour and normal samples are

collected. The procedure followed to collect these

data has been previously described in section 2.2.

After the acquisition stage, a pre-processing chain

is applied to the labelled dataset. In this pre-

processing stage an image calibration is done in order

to address the problem of the spectral non-uniformity

of the illumination device and the dark current.

Furthermore, a set of steps with the goal of removing

the noise of the spectral signatures and to reduce the

number of bands of the samples without lose the main

spectral information are applied.

Finally, so as to homogenize the spectral

signatures in terms of reflectance level, a pixel bright

correction step and a normalization step are

performed.

In the classification stage, the labelled dataset is

partitioned into two different datasets. Training

dataset is used to generate the classifier model while

test dataset is used to validate this model, obtaining

the results of the classification. Sensitivity, specificity

and overall accuracy are the evaluation metrics

chosen in order to know the goodness of the classifier

model. These evaluation metrics will be described

later.

Figure 5: Most representative bands of the VNIR hyperspectral image (400 nm to 1000 nm) from patient 12.

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Figure 6: Classification system overview.

3.1 Data Pre-processing

A pre-processing chain composed by three main steps

(image calibration, spectral noise and band reduction

and data normalization) has been developed in order

to homogenize the spectral signatures of the labelled

samples obtained from the in-vivo hyperspectral data-

cubes.

3.1.1 Image Calibration

The first step in the pre-processing chain is the image

calibration, where the significant signal variations

caused by the non-uniform illumination over the

surface of the captured scene are corrected. The

acquired raw image is calibrated using the white and

dark reference images.

White and dark reference images are acquired by

the demonstrator inside the operating theatre under

the same illumination conditions used to acquire the

in-vivo brain surface images. The white reference

image is obtained from a standard white reference tile

and the dark reference image is obtained by keeping

the camera shutter closed. The hyperspectral

calibrated image is calculated using the equation (1),

where CI is the calibrated image, RI is the raw image

and WR and DR are the white and dark reference

images respectively. Figure 7 shows the spectral

signature of a grade IV glioblastoma tumour tissue

before the calibration step (raw pixel) and Figure 8

after the applied calibration.

 =100∙

−

−

(1)

3.1.2 Noise and Dimensionality Reduction

The second step in the pre-processing chain is to

apply a series of filters in order to remove the noise

existing in the spectral signatures, mainly due to the

CCD sensor of the VNIR camera.

First of all, the noise filter which conforms the

first step of the HySIME algorithm is applied,

reducing a large amount of noise from the spectral

signatures. This function, which is named

Hyperspectral Noise Estimation, infers the noise in a

hyperspectral data set, by assuming that the

reflectance at a given band is well modelled by a

linear regression on the remaining bands (Bioucas-

Dias and Nascimento, 2008); (Nascimento and

Bioucas-Dias, 2015). Figure 9 shows the same

spectral signature after having applied this noise

filter.

Figure 7: Raw spectral signature of a grade IV glioblastoma

tumour tissue.

Figure 8: Calibrated spectral signature of a grade IV

glioblastoma tumour tissue.

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After this step, the bands from 0 to 50 and the

bands from 750 to 826 are removed since these bands

contain too much noise due to the limited

performance of the CCD sensor, the grate and the

light scattering in the extreme bands. This fact can be

seen in Figure 9. Additionally, this step reduces the

number of bands in the spectral signatures from 826

to 700 bands.

Afterwards, a smoothing technique is

independently applied to each pixel of the image. This

technique modified each pixel 



of the spectral

signature of the pixel under analysis, =

(



,

,

…,





), where k is the selected pixel and N

is the original number of bands. The new value of the

“smoothed point” (



)



is the average of the values

corresponding to predefined number of its

surrounding points, as shown in equation (2), where

 is number of bands to be combined.

(



)



=



/(2∙+1)





(2)

Due to the extremely high spectral resolution of

the images, it has been observed that consecutive

bands are correlated, providing redundant

information. In order to avoid this redundancy and

speed up the hyperspectral analysis of the data set, a

few bands have been removed. Moreover, it is not

needed to perform the smooth filter for those bands

that are going to be removed, which eases the filtering

process in terms of computational burden. In

particular 129 spectral bands, from the 700 spectral

bands previously processed, have been totally

filtered, uniformly covering the spectral range from

400 to 1000 nm as shown in Figure 10.

3.1.3 Data Normalization

Due to the surgery procedure, the pixels are captured

at different height, and hence, at different radiation

intensity. This fact typically causes that pixels

labelled as tumour and normal tissue have very

different radiation intensities. If these pixels are

introduced without any pre-processing in a classifier,

the pixels could be classified according to its

brightness, without really taking into account their

spectral signatures. In order to avoid this fact, a pre-

processing step which normalizes the brightness of

the pixels in the image needs to be included. This

process calculates the brightness of each pixel of the

hyperspectral image and divides each pixel by its

brightness, as shown in equation (3). In this equation





is the pixel with the brightness correction,  is the

pixel to be corrected and 



is the i-th component of

this pixel. With this pre-processing step, the

brightness of each pixel is homogenized without

modifying its spectral signature. Figure 10 illustrates

the final spectral signature with the full pre-

processing chain applied.









∑











(3)

Figure 11 presents the VNIR RGB image of the

patient 12, with the tumour area remarked (surgeon

prior evaluation), and the most representative features

of the final pre-processed data-cube. As it can be

appreciated, in feature 45 veins and tumour tissue

have a low brightness regarding to the normal tissue.

However, in feature 55 and 65, veins and normal

tissues have approximately the same brightness level

while the area where tumour is located exhibit lower

brightness. Feature 80 allows seeing veins in high

brightness conditions while tumour and normal

tissues have the same brightness. Finally, the feature

125 is relevant because where the tumour area is

located there is a high level of brightness. This fact

suggests that this pre-processed chain additionally

can obtain high level of contrast to distinguish

between veins, normal tissues and tumour tissues.

Figure 9: Spectral signature with the HySIME filter applied

to a grade IV glioblastoma tumour tissue.

Figure 10: Spectral signature with the noise and band

reduction step and the normalization applied to a grade IV

glioblastoma tumour tissue.

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Figure 11: Most representative features of the final pre-

processed image from patient 12.

3.2 Classification Algorithm

In this research work a supervised learning algorithm

has been employed, where the input features of the

classifier consist in the spectral signatures extracted

from brain tissue. The data mining algorithm chosen

for classifying data is Random Forest (RF). This

algorithm has been already used in the classification

of hyperspectral data (Ham et al., 2005). Random

Forest is an ensemble of Decision Trees (DTs), where

each tree has been generated with the same training

set, but is growing using different random vectors

(Breiman, 2001). A single Decision Tree handles

high-dimensional data well, has the ability to ignore

irrelevant features and provides an easy model

interpretation. However, DT usually has relatively

low prediction accuracy. Due to the advantages

provided by DT, many efforts to improve its

prediction accuracy has been proposed. It has been

discovered that one of the best ways to improve the

performance of Decision Tree-based algorithms is

using ensembles of DT, like Random Forest classifier

(Svetnik et al., 2003). The output of RF classifier is

calculated as the most popular class voted by the

trees.

Advantages of RF compared to other statistical

classifiers include very high classification accuracy;

a novel method of determining variable importance;

ability to model complex interactions among

predictor variables; flexibility to perform several

types of statistical data analysis, including regression,

classification, survival analysis, and unsupervised

learning; and an algorithm for imputing missing

values (Cutler et al., 2007).

3.3 Experimental Setup

The experimental setup chosen for this study merges

all available hyperspectral labelled data (from 13

different patients) in a single dataset. The dataset

employed in this research work has been created by

joining each single operation hyperspectral labelled

data, even if a unique class is given for a certain

operation. As the training and testing stages for

classification have been performed using data from

all the operations, the inter-patient variability of the

data will be taken into account.

The labelling of data has been performed using

two different abstraction levels of the diagnosis of the

tissue. In the first level, tissues have been grouped in

'Normal' tissues and 'Tumour' tissues, and the

classification using this labelling scheme has been

named 'Tag Level 1'. For the second labelling scheme,

'Tumour' tissues have been divided in 'Primary' and

'Secondary' tissues, attending to the diagnosis

provided by pathologists. This labelling scheme has

been named 'Tag Level 2'. The summary of the

dataset is given in Table 3.

In order to estimate the classifier performance,

and for obtaining the optimal configuration of the

selected algorithm, a three-way cross validation has

been employed. Three-way cross-validation consists

in two different stages for splitting the available

dataset: in the first stage, k-fold cross-validation is

used in order to get training and testing subsets. Test

data will be used to estimate the classifier

performance, and the training data are partitioned

again into training data and validation data (Figure

12). The training subset of the second cross-

validation stage is used to create the model of the

classifier, and the validation data are used to evaluate

the performance of the classifier. With the second

stage partition, the model fitting will be

accomplished, and the parameters of the classifier

will be modified in order to obtain the optimal

configuration of the algorithm. The test set obtained

in the first dataset split, is used to perform the

performance evaluation of the algorithm by using

unknown data for the classifier. The k value selected

for both cross-validations is 10, which is a typical

value used in data mining

Figure 12: Three-way cross-validation experimental setup

overview.

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317

Table 3: HELICoiD Labelled Dataset with two

classification tag levels.

#Operation

Diagnosis

#Samples

Tag Level 1 Tag Level 2

Normal 408

Tumour Secondary 578

Normal 1939

Tumour Secondary 522

Normal 832

Tumour Secondary 493

6 Normal 806

7 Normal 768

Normal 1484

Tumour Primary 3259

10 Tumour Primary 425

Normal 806

Tumour Primary 1424

13 Tumour Secondary 390

14 Tumour Primary 1139

Normal 648

Tumour Primary 800

Normal 4913

Tumour Primary 1987

17 Tumour Primary 1025

Total 24646

3.4 Evaluation Metrics

The goodness of the classifier has been measured

using sensitivity, specificity and overall accuracy

metrics. Sensitivity measures the test ability to

identify a condition correctly. It is computed as

follows:

 =



+

(5)

where TP is the number of true positives and FN is

the number of false negatives in a population.

Specificity measures the test ability to exclude a

condition correctly. It is expressed as follows:

=



+

(6)

where TN is the number of true negatives and FP is

the number of false positives in a population.

Finally, the equation (7) shows the accuracy

metric that represents the percentage of total correctly

classified samples in a population:

 =

+

+++

(7)

4 EXPERIMENTAL RESULTS

The hyperspectral classification experiments have

been performed using the three different pre-

processed data previously described. The calibrated

data are the labelled samples with only the white and

dark calibration applied. HySIME filtered data are the

previous calibrated samples with the HySIME filter

applied over them. The last set of data has the

complete pre-processed chain applied, this set of data

is called pre-processed samples.

As it was commented previously, two different

levels of diagnosis detail have been evaluated.

4.1 Tumour Vs Normal: Tag Level 1

In this section will be presented the results obtained

using the Random Forest classification system over

the three different set of data taking into account the

first tag level (normal vs tumour tissues).

The results of the classification in this case study

shows that an automatic discrimination between

'Normal' tissue and 'Tumour' tissue is possible using

the hyperspectral signature of the tissues. Sensitivity

and specificity lie in the same range, which means

that the algorithm is capable to identify both kinds of

tissues.

Although the classification results provide

accurate discrimination rates in terms of accuracy,

specificity and sensitivity, varying the pre-processing

stage results in an improvement of the classification.

From data shown on Table 4, it can be seen that the

accuracy improves from around 93%, when the pre-

processing chain consists only of the calibration of

the hyperspectral image, to 99% when using the

whole pre-processing chain presented in Figure 6.

Figure 13 presents these results in a bar chart.

Figure 13: Comparison between the classification results of

the three data sets at tag-detail level 1.

93,67%

96,06%

99,68%

94,05%

95,67%

99,68%

93,24%

96,49%

99,67%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Calibrated HySIME Filtered Pre-processed

Accuracy (%) Sensitivity (%) Specificity (%)

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Table 4: Comparison between the classification results of

the three data sets at the tag level 1.

Calibrated HySIME Filtered Pre-processed

Sensitivity (%)

94.05 95.67 99.68

Specificity (%)

93.24 96.49 99.67

Accuracy (%)

93.67 96.06 99.68

This trend is kept for the rest of the evaluation

metrics: specificity improves from 93% to 99% and

sensitivity improves from 94% to 99%, when the full

pre-processing chain is used, instead of performing

only the calibration.

4.2 Normal Vs Primary Vs Secondary:

Tag Level 2

The same data have been classified with a different

tag scheme, where 'Tumour' tissues have been divided

into 'Primary' and 'Secondary' tumour tissue labels.

Figure 14 illustrates the accuracy results between the

different pre-processed datasets and Table 5 to Table

7 show the classification results in terms of sensitivity

and specificity for each class. These data have been

obtained by calculating the confusion matrix of each

dataset that can be seen in Table 8. The error

estimation of this classification shows that the

algorithm can keep a good performance on

discriminating between normal and tumour tissue,

even using a more complex labelling scheme. The

sensitivity and specificity values show also high

values, which indicates that all classes have been

properly classified. Results show again that the used

pre-process chain improves the results of the

classification.

Figure 14: Accuracy comparison between the classification

results of the three data sets in tag level 2.

Table 5: Classification results of the calibrated dataset in

tag level 2.

Sensitivity (%)

Normal Primary Secondary

Specificity (%)

Normal

- 94.67 99.67

Primary

94.63 - 94.34

Secondary

97.64 89.21 -

Table 6: Classification results of the HySIME filtered

dataset in tag level 2.

Sensitivity (%)

Normal Primary Secondary

Specificity (%)

Normal

- 96.07 99.51

Primary

95.49 - 96.4

Secondary

99.45 91.88 -

Table 7: Classification results of the pre-processed dataset

in tag level 2.

Sensitivity (%)

Normal Primary Secondary

Specificity (%)

Normal

- 99.92 100.00

Primary

99.31 - 99.90

Secondary

100.00 100.00 -

5 CONCLUSIONS

In this paper, it has been described a hyperspectral

acquisition system used to create a hyperspectral

database of human brain tissues previously diagnosed

as tumour or normal. In each surgical procedure, a

few rubber ring markers have been placed by the

neurosurgeons to get assessed diagnosis from

pathology. Some of these markers were located in

areas of brain where neurosurgeons were quite sure

that the tissues were healthy, and the other markers

were placed where the resection was going to be

performed. A biopsy from the rejected tissue was sent

to pathology, providing assessed diagnosis of the

tissues inside the marker. These samples were used as

the ground truth for classification.

Table 8: Confusion matrix of the three types of datasets in tag level 2.

Predicted Results

Calibrated HySIME Filtered Per-Processed

Normal Primary Secondary Normal Primary Secondary Normal Primary Secondary

Gold

Standard

Normal 1225

52 3

1223

43 1

1249

7 0

Primary

917

15 50

910

16 1

1013

Secondary

4 55

124

6 34

181

0 1

193

91,96%

93,91%

99,63%

20%

40%

60%

80%

100%

Calibrated HySIME Filtered Pre-processed

Accuracy (%)

A Novel Use of Hyperspectral Images for Human Brain Cancer Detection using in-Vivo Samples

319

Taking into account the diagnostic information

provided by pathologists, the pixels inside markers

were extracted from the hypercubes and labelled

according to the diagnosis. Due to the complexity of

the diagnostic information, a labelling scheme

consisting in two abstraction levels of disease details

had been proposed.

The classification results shown in section 4 show

that it is possible to obtain an accurate and automatic

discrimination between different types of tissues

using the labelling schemes proposed. Although the

three proposed pre-processing chains provided

accurate classification results (accuracy higher than

89% for all the classifications), the more complex one

provided the best classification results in all the

experiments exposed in this paper.

In the near future, some additional research is

foreseeable to be done. Firstly, the complexity of the

diagnosis can be further explored. For instance,

primary tumours could be classified according to its

Grade, and Secondary tumours (metastasis) could be

differentiated attending to their origin (breast, lung,

etc.). The next step will be to define a more complex

labelling scheme to better classify the type of tumour.

Secondly, we are working in the design a case study

where the automatic diagnostic of a new patient could

be computed by using a model that had been created

using the hyperspectral data from previous (and in

consequence different) patients. Thirdly, it could be

interesting to test the performance of other different

machine learning algorithms, like the support vector

machines (SVM), the neural networks (NN), etc.

Finally, due to the large experience that the research

group has in hardware implementations, we are

considering the implementation of the pre-processing

and classification algorithms in some hardware

platform (FPGA, GPU, ASICs, many cores, etc.) to

accelerate its execution.

ACKNOWLEDGEMENTS

This work has been supported in part by the European

Commission through the FP7 FET Open programme

ICT-2011.9.2, European Project HELICoiD

“HypErspectral Imaging Cancer Detection” under

Grant Agreement 618080.

REFERENCES

G. Lu, B. Fei, 2014. Medical hyperspectral imaging: a

review. Journal of biomedical optics, vol. 19, no. 1, pp.

010 901–010 901.

H. Akbari, L. V. Halig, D. M. Schuster, A. Osunkoya, V.

Master, P. T. Nieh, G. Z. Chen, and B. Fei, 2012.

Hyperspectral imaging and quantitative analysis for

prostate cancer detection. Journal of biomedical optics,

vol. 17, no. 7, pp. 0 760 051–07 600 510.

B. Fei, H. Akbari, L. V. Halig, 2012. Hyperspectral imaging

and spectral-spatial classification for cancer detection.

Biomedical Engineering and Informatics (BMEI), 5th

International Conference on. IEEE, pp. 62–64.

M. E. Martin, M. B. Wabuyele, K. Chen, P. Kasili, M.

Panjehpour, M. Phan, B. Overholt, G. Cunningham, D.

Wilson, R. C. DeNovo, & T. Vo-Dinh, 2006.

Development of an advanced hyperspectral imaging

(HSI) system with applications for cancer detection.

Annals of Biomedical Engineering, 34(6), pp. 1061–

1068.

B. D. de Dinechin, R. Ayrignac, P.-E. Beaucamps, P.

Couvert, B. Ganne, P. G. de Massas, F. Jacquet, S.

Jones, N. M. Chaisemartin, F. Riss, T. Strudel, 2013. A

clustered manycore processor architecture for

embedded and accelerated applications. High

Performance Extreme Computing Conference (HPEC),

IEEE, pp.1-6.

J. M. Bioucas-Dias and J. M. Nascimento, 2008.

Hyperspectral subspace identification. Geoscience and

Remote Sensing, IEEE Transactions on, vol. 46, no. 8,

pp. 2435–2445.

J. M. Nascimento and J. M. Bioucas-Dias, 2015.

Hyperspectral noise estimation.

https://github.com/jhausser/ParTI/blob/master/mvsa_d

emo/estNoise.m, last accessed: November 2015.

JiSoo Ham, Yangchi Chen, Melba M. Crawford, 2005.

Investigation of the Random Forest Framework for

Classification of Hyperspectral Data. IEEE

Transactions On Geoscience and Remote Sensing, Vol.

43, No. 3, pp. 492 – 501.

Breiman, L., 2001. Random forests. Machine learning, Vol.

45, No 1, pp. 5-32.

Svetnik, V., Liaw, A., Tong, C., Culberson, J. C., Sheridan,

R. P., & Feuston, B. P., 2003. Random forest: a

classification and regression tool for compound

classification and QSAR modeling. Journal of chemical

information and computer sciences, Vol. 43, No 6, pp.

1947-1958.

R. Cutler, T. C. Edwards, K. H. Beard, A. Cutler, K. T.

Hess, J. Gibson and J. J. Lawler, 2007. Random Forests

for Classification in Ecology. Ecology, Ecological

Society of America, Vol. 88, No. 11, pp. 2783-2792.

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