Advances in Multimedia

Volume 2018, Article ID 4976372, 8 pages

https://doi.org/10.1155/2018/4976372

## Performance Evaluation of Contour Based Segmentation Methods for Ultrasound Images

^{1}Department of Biomedical Engineering, Sathyabama Institute of Science and Technology, Chennai, TamilNadu-600, India^{2}Department of Electronica and Telecommunication, Sathyabama Institute of Science and Technology, Chennai, TamilNadu-600, India^{3}Department of Biomedical Engineering, Vels Institute of Science, Technology and Advanced Studies, Chennai, TamilNadu-600117, India

Correspondence should be addressed to R. J. Hemalatha; moc.liamg@ahtalamehjr

Received 26 May 2018; Revised 9 August 2018; Accepted 30 August 2018; Published 16 September 2018

Academic Editor: Huiyu Zhou

Copyright © 2018 R. J. Hemalatha et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

#### Abstract

Active contour methods are widely used for medical image segmentation. Using level set algorithms the applications of active contour methods have become flexible and convenient. This paper describes the evaluation of the performance of the active contour models using performance metrics and statistical analysis. We have implemented five different methods for segmenting the synovial region in arthritis affected ultrasound image. A comparative analysis between the methods of segmentation was performed and the best segmentation method was identified using similarity criteria, standard error, and F-test. For further analysis, classification of the segmentation techniques using support vector machine (SVM) classifier is performed to determine the absolute method for synovial region detection. With these results, localized region based active contour named Lankton method is defined to be the best segmentation method.

#### 1. Introduction

Musculoskeletal disorder (MSD), an epidemic disease, is now a significant health problem in emerging and most developing countries in the world. MSD remains the most prevalent disease in society due to its impact on mobility, ability to work, and life style. Arthritis is one of the prevalent MSD among all the age groups of people [1]. The most affected portion is the joint region. At relatively early stage of the disease the synovial membrane around the joints used to inflame and leads to degeneration of the joints. The main system to visualize the joint state is through ultra sound diagnosis (USD). The USD is less expensive and available with all the clinicians [2, 3]. The USD images represent different tissue regions with variations of gray shades. These images are further processed to segment the synovial region and to analyze the disease condition and further progression of the disease.

One of the essential tasks in image analysis is image segmentation. Segmentation is to extract objects from the images by dividing the image into set of regions with different properties. Segmentation plays an important role in automatic object recognition or pattern identification process to identify pathologies and medical diagnosis [4, 5]. The most challenging task is to extract the contour and boundaries of the desired region for dynamic analysis of anatomical structures. One of the most robust segmentation methods for medical images is active contour method (ACM).The method implies curve evolution to detect the region of interest in a given image [6–8]. The segmentation process is based on edge and region based approach. The geometric active contour model proposed by Caselles and Malladi et al. is an edge based approach which is based on evolution of curves and geometric flows [9]. Chan and Vese have proposed edge less active contour model which is one of the most well-known region based method. Bernard et al. have proposed a parameterized active contour method. More recently Li et al. and Lankton proposed a method which utilizes local region information for segmentation [9–14].

In this paper we have applied different segmentation methods to arthritis affected finger joint ultrasound images to segment the synovial region. The efficiency of these methods is analyzed using performance analysis metrics and statistical analysis method. For performance analysis metrics we have used Dice coefficient and Hausdorff coefficient and, for statistical analysis method standard error, F-test were used. At the end classification is used to define the absolute active contour method for segmentation of synovial region by training and analyzing the performance metrics and statistical values. The rest of the paper is organized as follows Section 2. The Methods and Materials, Section 3 the results and discussion, and Section 4 the conclusion.

#### 2. Methods and Materials

In medical imaging, the segmentation of regions with specific parameters is carried out with the help of active contour models. Because these models develop a contour around the target object and segregate it from the image, the segmented image possesses only the required information of the target object [15]. The level set segmentation methods like Caselles, Chan–Vese, Bernard, Li, and Lankton are applied on arthritis affected finger joint images obtained from the MEDUSA database http://medusa.aei.polsl.pl.[16–18]. Further using performance analysis metrics like dice coefficient and Hausdroff distance and statistical analysis metrics like standard error and F-test describes the significant difference between the techniques used for segmentation. Classification using SVM defines the best suited method for synovial region segmentation.

MEDUSA is a standardized and authorized database which consists of finger joint images of different grades (grade 0, grade 1, grade 2, and grade 3) of synovitis. Various studies related to arthritis and synovitis are performed using this database.

##### 2.1. Caselles

Caselles is geodesic based active contour methods which largely depend on the level set functions that describe the specific regions in the image for segmentation. Contours are described based on the geometric flow of curve and detection of objects in the image [11]. This type of contour model modifies the curve in the plane by moving the points of the curve perpendicular. The motion of the points is at a speed proportional to the curvature of the region in the image. By adding an area of minimizing region (balloon force), propagation of contour occurs internally by minimization of the interior energy given by I is image intensity, G is Gaussian Filter of unity variance, and C is derived parametric curve to regions with high gradient where set level function is executed as a signed distance function (P= )

Contour models use the energy forces for geometric flow curve description. Geometric contours can be obtained based on regions and edges in the curvature of the image [12].

##### 2.2. Chan–Vese

Chan–Vese is a region based method which segments an image into two homogeneous regions. The method utilizes energy minimization technique defined by weighted values corresponding to the average value of sum of intensity difference from outside and inside the segmented region [9, 10]. Contours are based on either the variance inside and outside contour or the squared difference between average intensities inside and outside the contours along with the total contour length. This contour model helps to determine different image properties, not only edges, and it also includes regions based on texture and other geometrical features. Energy defines the entire region of interest from the image.

The total energy of the model is given in *μ*, *ν*, *λ*_{1}, *λ*_{2}: real parameters C_{1}, C_{2}: constants determined for segmentation −1≤ *φ*(a)≤1: level set function in which *φ*(a)=0 specifies the interface f: original image H: heavy side function in 1 dimension centered at 0 and *δ*=H′.

##### 2.3. Bernard

Bernard method utilizes B-spline coefficients as energy minimization function. These utilize parameterized active contour method [12]. Spline coefficients define the contour models for the pixels of interest. The energy based functions inside and outside is described with these coefficients. Contour models describe the entire structures with inflation force that can overpower forces from weak edges, amplifying the issue with localization of initial guess. To speed up the process a linear combination of B-spline basis functions is used and given in

Φ(a) is linear combination of B-spline basis functions.

##### 2.4. Chumming Li

In order to separate the region into two homogenous regions this method utilizes using local neighbourhood statistics for each pixel given in (6). It uses local region information for segmentation [13]. The energy function of the region based active contour model is range of region based domain kernel function. By minimizing the energy function, the region of elements of the target could be determined in images with contours. I(a): pixel intensity at x H: heavy side function : Gaussian Kernel.

##### 2.5. Lankton

Lankton is a region based active contour method which segments non homogeneous objects. This method utilizes localizing region based energy which segments the region based on local information. It is not suitable for unsupervised image segmentation as it requires appropriate curve initialization [14]. These models form contour boundaries with energy forces required for the particular region of interest. The energy inside and outside depends on the local region pixels of the image that describes the required region. The energy equation is illustrated in *δ* is Dirac function B is Ball of radius r centered at point x

##### 2.6. Performance Evaluation Metrics

The segmentation methods are qualitatively and quantitatively assessed and compared with each other based on three kinds of criteria. Based on this the best suited algorithm is chosen for particular applications.

**Visual criteria:** The segmented region using the active contour methods is compared with the annotated images by expert radiologist as reference image. The segmented region obtained from level set function methods is compared with reference image.

**Computation time:** The time taken for each algorithm to segment the region represents the speed of the algorithm. The speed of the respective algorithm is compared.

**Similarity criteria:** This criterion measures the similarity between the reference and segmented image. The quality of the segmented image is measured by calculating the Dice coefficient, Hausdorff distance, and PSNR

Dice coefficient compares the segmented region with the reference region from the annotated image and provides the dice coefficient values ranging between 0 and 1. If it is 1 the segmented region is more similar and it is different when it is 0 [19].Hausdroff distance is a metric to measure dissimilarity between two point sets. Distance transform is used to compute the HD in an image. This is used to control the progress of level set based algorithms and to evaluate the quality of the clusters [20].

##### 2.7. Statistical Analysis

The features like mean, variance, and standard errors are calculated for the segmentation methods. Among the five segmentation methods the best suited method was identified using statistical analysis.

**Mean: **The mean value is termed as average value which is computed by taking sum of all perceived outcomes divided by overall number of gray levels. The following shows mathematical expression for mean represented as:where n is sample size and x is observed value.

**Variance** is study of deviation of actual value versus predicted value. The deviation from actual and predicted indicates the performance of the methods used.

**Standard error** is defined as the measure of prediction’s accuracy. Estimated standard error is related to sum of squared deviations of prediction (that is sum of squares error), described is the standard error of the estimate, Y is an actual range, Y′ is a predicted range, and N is the number of pairs of scores. is the sum of squared differences between the actual scores and the predicted scores.

##### 2.8. Classification

Classification is a process to describe the effective type or class based on the features derived from the region of interest. Support vector machines (SVM) are machine learning models. SVM is the representation of observations as points that maps to form separate divisions and a clear boundary factor defined as decision boundary. Multiclass support vector machine classifies the types based on the kernel models. Multiclass support vector machine is used to illustrate the appropriate type of active contour technique for the segmentation of the synovial region.

#### 3. Results

In this method, different types of active contour segmentation techniques are used for the detection of synovial region. The segmentation methods were evaluated using performance metrics and statistical analysis. Ultrasound images from the database are used for the identification of synovial region. Fifty images of different grades are considered for segmentation of the synovial region from the database. Different active contour segmentation techniques are used to segment the synovial regions. Visual changes in the segmentation process are illustrated through the images in Figure 1.