Abdul Kadir Jumaat

Comparison of Balloon Snake and GVF Snake in Segmenting Masses from Breast Ultrasound Images

The active contour or Snake is a computational model that can trace boundaries of images. It is formulated based on controlled continuous splines and adopts energy minimization conception. This paper presents the application of Snakes for the segmentation of masses on breast ultrasound images. The boundaries of the masses identified may be used in locating potential cancerous cases for further analysis. Initially, Balloon Snake and Gradient Vector Flow (GVF) Snake are applied in segmenting the masses in the breast ultrasound phantom images. Comparison on the masses areas segmented by the Balloon Snake and the GVF Snake is done against the actual masses area. The better method with smaller values of average percentage area difference is chosen to be applied in segmenting masses on real breast ultrasound images. The performance is measured in terms of average percentage area difference traced by the chosen method against the area traced by expert radiologist. It is found that from eighty breast ultrasound phantom images (40 cyst and 40 solid masses) tested, the values of average percentage area difference for cyst and solid masses in the Balloon Snake are 3.07% and 9.17% respectively while GVF Snake are 13.43% and 48.37% respectively. Therefore Balloon Snake is chosen to segment real breast ultrasound images. Fifty images are tested. Segmentation on the images shows that the average percentage area difference of Balloon Snake is 4.29% which mean 95.71% accurate.

Seed point selection for seed-based region growing in segmenting microcalcifications

Seed-based region growing (SBRG) has been widely used as a segmentation method for medical images. The selection of initial seed point in SBRG is the crucial part before the segmentation process is carried out. Most of the region growing methods identify the seed point manually which involve human interaction and require prior information about the image. In this paper, an automated initial seed point selection for SBRG algorithm is proposed. The proposed method is tested on 50 mammogram images confirmed by a radiologist to consist microcalcifications. The performance is evaluated using Receiving Operator Curve (ROC) based on level of detection. Experimental results show that the method has successfully segmented the microcalcifications with 0.98 accuracy.

Segmentation and characterization of masses in breast ultrasound images using active contour

The active contour or Snake is a computer generated curve, used to trace boundaries of images. This paper presents the application of Snake for the segmentation of masses on breast ultrasound images and the characterization of the segmented masses as malignant or benign. Initially, the Balloon Snake is chosen to segment the masses. Comparison on the masses areas segmented by the Balloon Snake is done against the areas traced by radiologist. Experimental result shows that from fifty masses tested, the Balloon Snake successfully segment the masses with accuracy of 95.71%. Then, a mass is characterized as benign or malignant using a proposed method namely the semi-automated characterization (SAC) method. The method is based on the segmented masses produced by the Balloon Snake. The criterion of angular margin is considered in characterizing the masses as malignant or benign by the SAC method. The characterization reading of a mass by the SAC method is compared with thirty sets of characterization readings of a mass by different radiologists. The comparison is made in terms of sensitivity and specificity values. Based on the values, the receiver operating characteristics (ROC) curve is plotted for each set of comparison. From the thirty sets of comparisons, it is found that the area under curve of all the thirty ROC curves are greater than 0.7. The value implies that the SAC method gives high accuracy in characterizing benign from malignant mass. Since the method is based on the segmented masses by the Balloon Snake, the value also implies that the accuracy of Balloon Snake in segmenting the images is high (95.71%).

Classification of breast abnormalities using artificial neural network

Classification is the process of recognition, differentiation and categorizing objects into groups. Breast abnormalities are calcifications which are tumor markers that indicate the presence of cancer in the breast. The aims of this research are to classify the types of breast abnormalities using artificial neural network (ANN) classifier and to evaluate the accuracy performance using receiver operating characteristics (ROC) curve. The methods used in this research are ANN for breast abnormalities classifications and Canny edge detector as a feature extraction method. Previously the ANN classifier provides only the number of benign and malignant cases without providing information for specific cases. However in this research, the type of abnormality for each image can be obtained. The existing MIAS MiniMammographic database classified the mammogram images into three features only namely characteristic of background tissues, class of abnormality and radius of abnormality. However, in this research three ot...

Performance comparison of Canny and Sobel edge detectors on Balloon Snake in segmenting masses

The most common problem in image processing is image segmentation. One of the methods which can segment an image with a high accuracy is Balloon Snake. It uses the energy minimization conception where it has a dynamic behavior that deforms from an initial position and converges to the boundary of the object in an image. However, we found that the accurateness always influences by the edge map detector that being used in implementing the Balloon Snake. Edge map detector is used to strengthen the boundary of the object before the object is segmented. Two popular edge map detectors are chosen in this research namely Canny and Sobel edge detector. Both edge detectors are implemented in Balloon Snake in segmenting 40 masses in breast ultrasound images. The pixel area traced by the two combination methods namely Canny and Balloon Snake and Sobel and Balloon Snake are evaluated. The accuracy is measured based on the percentage pixel area difference between radiologist and the both combination methods. It is found that the combination of using Canny and Balloon Snake give 3.5% percentage of pixel area difference which is smaller compared to the other combination which have 12.7% percentage pixel area difference.

Microcalcifications segmentation using three edge detection techniques

Edge detection has been widely used especially in medical image processing field. In this paper we are comparing Sobel, Prewitt and Laplacian of Gaussian (LoG) edge detection techniques in segmenting the boundary of microcalcifications. The edge detection must satisfy the breast phantom scoring criteria before the segmentation phase is carried out. Then, all of the edge detection techniques are implemented in the Enhanced Distance Active Contour (EDAC) model for the segmentation process. Results obtained from Area Under the Curve (AUC) of the Receiver Operating Characteristic (ROC) curve shows that the Prewitt edge detection has the highest value of AUC, followed by the Sobel and LoG which are 0.79, 0.72 and 0.71 respectively.

A NUMERICAL STUDY ON PREDATOR PREY MODEL

Stochastic spatial models are becoming a popular tool for understand the ecological and evolution of ecosystem problems. We consider the predator prey interactions in term of stochastic representation of this Lotka-Volterra model and explore the use of stochastic processes to extinction behavior of the interacting populations. Here, we present simulation of stochastic processes of continuous time Lotka-Volterra model. Euler method has been used to solve the predator prey system. The trajectory spiral graph has been plotted based on obtained solution to show the population cycle of predator as a function of time.

Comparison between GVF snake and ED snake in segmenting microcalcifications

Snake, active contour or deformable active contour has been widely used in medical image segmentation area. In this paper, comparison between Gradient Vector Flow (GVF) snake and Enhanced Distance (ED) snake in segmenting microcalcifications is carried out. The performance is measured based on actual area of the average percentage difference traced by expert radiologists. Results obtained shows that the values of average percentage difference for the GVF and ED snake are 4.3% and 6.68% respectively. These results indicate that the GVF snake has better performance with 95.7%.