٭Department of Electrical & Control Instrumentation, Kaunas University of Technology, Lithuania * Intelligent Systems Laboratory, Halmstad University,

Department of Electrical & Control Instrumentation, Kaunas University of Technology, Lithuania٭* Intelligent Systems Laboratory, Halmstad University, Sweden

° Department of Otolaryngology, Kaunas University of Medicine, Lithuania

Categorizing Sequences of Laryngeal Data

for Decision SupportA. Gelžinis٭, E. Vaičiukynas٭, E. Kelertas٭, M. Bačauskienė٭, A. Verikas*٭, V. Uloza°, A. Vegienė°

The 4th International Conference onElectrical & Control Technologies

May 8, 2009 Categorizing Sequences of Laryngeal Data for Desicion Support 2

COST Action 2103• Can we be more dedicated 2 basic human needs?• eHealth investigates modern best practices in the

use of Information and Communication Technologies (ICT) as tools for enhancing health promotion, health protection, quality of service, accessibility and efficiency in all aspects of health care delivery (to meet all health needs of society).

• COST Action 2103 “Advanced Voice Function Assessment” combine previously unexploited techniques with new theoretical developments to improve voice production models and analysis algorithms with a view to voice quality & diseases.


Decision Support System• In clinical practice, decision making is quite often

based on subjective evaluation of patient data.• Long-term goal of our work is decision support

system for automated diagnostics of laryngeal diseases, using such information sources:• voice signal of patient (recorded with microphone);• images of vocal folds (laryngeal videostroboscopy);• questionnaire data.

• In this experiment we explore kernel-based correlative learning techniques on information extracted from videostroboscopy (sequences of images) for categorizing laryngeal disorders.


Laryngeal Videostroboscopy• Well-established technique for measuring the

glottal gap, glottic closure, glottic angle and the angular velocities of vocal fold abduction and adduction (with single-flash-timing).

• Multiple fundamental frequencies may be recorded in the case of some diseases, such as polyps, nodules, and cysts (with multiple-flash-timing).

• Duration = 20 – 80 seconds• Resolution = 768 × 576 pixels• Frame rate = 25 frames / second• Data used is from Department of Otolaryngology,

Kaunas University of Medicine, Lithuania.


Categories of Laryngeal Data

• Mass lesions of vocal folds = [ healthy | diffuse | nodular ]

• Nodular lesions (localized thickenings):• nodules• polyps• cysts

• Diffuse lesions:• papillomata• hyperplastic• laryngitis w/ keratosis• carcinoma


Healthy | Diffuse | Nodular


Color Space & Features• Same incident light - different RGB values (by

different acquisition equipment). Transform space!• Similarity of two colors from their distance in the

space is evaluated by using L*a*b color space.• Multi-scale retinex theory-based color image en-

hancement mitigates capturing condition variation.• Color distribution is expressed by 3D color

histogram of N bins as a random N-vector, which consists of N = 4096 (16×16×16) bins.

• Compact description of histogram is obtained by removing not so important bins from all images.


Nonlinear Features Extraction• Kernel Principal Component Analysis (Scholkopf)

• orthogonal transformation of coordinate system of data• powerful method of extracting nonlinear features for

classification (finding nonlinear relationships in data)• nonlinear generalization of PCA via the kernel trick• linear PCA in the feature space = KPCA in the input• helps avoid overfitting the distributions in the possibly

infinite-dimensional feature space F (or RKHS)• only the first few important eigenvectors are kept,

effectively regularizing the resulting distributions• first principal component = most of variability in the data

• Solving the eigenvalue problem of kernel PCA is to diagonalize the kernel (data covariance) matrix.


Kernel Methods for Classification

kernelpatternanalysisalgorithm

identifiedpattern

(data class)function

data subspacesubspace

K(X,Z) K LS-SVM f(x)=Σαiκ(xi,x)

Kernel trick: K(X, Z) = ‹ K(·,X), K(·,Z) › = ‹ φ(X), φ(Z) ›


Support Vector Machine Classifier


Kernel Trick in SVM• Primal representation (inputs)

• Dual representation (features)


Primal & Dual Problems in SVM

• Primal problem (margin maximization)

• Dual problem (quadratic programming)


SVM Solution – Support Vectors

• Lagrangian with Lagrange multipliers α:

• Conditions for optimality (saddle point):

• Primal representation / Dual representation


Least Squares SVM (LS-SVM)

• Primal problem (margin maximization)

• Dual problem (quadratic programming)


• Lagrangian with Lagrange multipliers α:

• Conditions for optimality (saddle point):

• Primal representation / Dual representation

LS-SVM Solution – Support Vectors


Ensemble-based Matching• Ensemble matching methods generally consider

the task of obtaining a similarity function which operates on pairs of sets of feature vectors, or pairs of ensembles.

• Video is considered as an ensemble of feature vectors, where each vector is extracted from corresponding video frame – image.

• The alignment of the two subspaces spanned by the elements of the two ensembles is used as a measure of similarity.

• The kernel principal angle is the angle between the principal subspaces of two matrices, each matrix composed of feature vectors as columns.


Kernel Principal Angles

• Principal angles are angles between a pair of vector sets in two linear subspaces (also relates to principal correlation).

• If θ = 0, the cosine is 1 and vector sets are parallel.• If θ = π/2 , the cosine is 0 and vector sets are orthogonal.

• Quantities cos(θk) are often referred to as principal correlations or canonical correlations of the matrix pair.


Sequence Kernel Function (KPA)• Kernelized Modified Gram-Schmidt Orthogonalization used

• Kernel trick was used to compute principal angles in the feature space induced by a minor Gaussian (RBF) kernel:

K(x, z) = ‹ φ(x), φ(z) › = kminor(x,z)

• Instead of dealing with the set-elements x in input space, we work with their mappings in feature space Φ(x), which are accessible only as scalar products.

• Using a linear minor kernel (polynomial degree 1) is equivalent to computing principal angles in input space.

• Positive-definite kernel (similarity metric) for our LS-SVM:• K (Ai, Aj) ≡ f (Ai, Aj) = ∏k=1..n cos2(θk)


Experimental Setup• Available data from 30 patients has:

• 10 patients evaluated as belonging to the nodular class;• 12 patients evaluated as belonging to the diffuse class;• 8 patients evaluated as belonging to the healthy class.

• There were 100 image frames in each 4 s image sequence, but only 10 from the middle were used.

• Data normalized to 0 mean and variance 1.• Polynomial kernel of degree q = 1, 2, 3 in KPCA.• Gaussian kernel as a minor kernel to estimate the

KPA kernel (measure over a pair of matrices).• Due to the small number of data points (image

sequences) leave-one-out test was chosen for %.


Results – Number of KPC


Results – Number of KPC & Gamma


Results – Gamma & Sigma


Summary of Experiment• Least squares support vector machine (LS-

SVM) was constructed in Matlab for classification into 3 classes, namely healthy | nodular | diffuse.

• Kernel function (KPA kernel) employed by LS-SVM was designed to be used over a pair of matrices, rather than over a pair of vectors, to kernelize principal angles between subspaces.

• Different number of kernel principal components was extracted from N-vector histogram & tested.

• Disease recognition is done on the premises, that:• different disorders generate different subspaces• principal angles between subspaces can be measured


Conclusions• In this paper, we have investigated an approach to

an automated analysis of sets of vocal cord images aiming to categorize laryngeal disorders.

• Sequences of color laryngeal images were categorized into the healthy, nodular and diffuse classes using an LS-SVM with a kernel defined over a pair of matrices.

• Bearing in mind the high similarity of the decision classes, encouraging classification performance was obtained when testing the developed tools on data recorded during routine videostroboscopy.

• Relatively high classification accuracy obtained (85,7%) encourages further studies in this area.

Documents

٭Department of Electrical & Control Instrumentation, Kaunas University of Technology, Lithuania * Intelligent Systems Laboratory, Halmstad University,