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%)

Review methods for image segmentation from computed tomography images

Image segmentation is a challenging process in order to get the accuracy of segmentation, automation and robustness especially in medical images. There exist many segmentation methods that can be implemented to medical images but not all methods are suitable. For the medical purposes, the aims of image segmentation are to study the anatomical structure, identify the region of interest, measure tissue volume to measure growth of tumor and help in treatment planning prior to radiation therapy. In this paper, we present a review method for segmentation purposes using Computed Tomography (CT) images. CT images has their own characteristics that affect the ability to visualize anatomic structures and pathologic features such as blurring of the image and visual noise. The details about the methods, the goodness and the problem incurred in the methods will be defined and explained. It is necessary to know the suitable segmentation method in order to get accurate segmentation. This paper can be a guide to researcher to choose the suitable segmentation method especially in segmenting the images from CT scan

Effects of image processing techniques on mammographic phantom images: a pilot study

Breast cancer is one of the most important diseases among females. According to the Malaysian Oncological Society (Wahid, 2007), about 4% of women who are 40 years old and above are suffering from breast cancer. Masses and microcalcifications are two important signs for breast cancer diagnosis on mammography. In this research, the effects of different image processing techniques which include enhancement, restoration, segmentation, and hybrid methods on phantom images were studied. Three different phantom images, which were obtained at 25kv (63.2 MAS), 28kv (29.8 MAS) and 35kv (9.5 MAS), were manipulated using image processing methods. The images were scored by two expert radiologists and the results were compared to explore any significant improvements. Meanwhile, the Wilcoxen Rank test was used to compare the quality of the manipulated images with the original one (alpha=0.05). Each image processing method was found to be effective on some particular criteria for image quality. Some methods were effective on just one criterion while some others were effective on a few criteria. The statistical test showed that there was an average improvement of 41 percent when the images were manipulated using the histogram modification methods. It could be concluded that different image processing methods have different effects on phantom images which generally improve radiologists’ visualization. The results confirm that the histogram stretching and histogram equation methods lead to higher improvement in image quality as compared to the original image (p < 0.05)

Evaluation of performance for different filtering methods in CT brain images

This paper presents the comparison of filtering methods for a contrast enhancement of computed tomography (CT) brain images. Each method consists of three filter consecutively which is a combination of the low order linear filter such as Gaussian filter, disk filter, average filter and median filter with an adaptive filter method and unsharp filter. The process starts with filtering the CT brain image using low order linear filter, then proceeds with adaptive averaging filter and ends with unsharp filter. In this paper, there are two criteria, peak signal to noise ratio and mean square error, that were adopted for performance assessment. Our preliminary results showed that the combination of Gaussian filter with adaptive filter and unsharp filter gives the good result in removing the noise and edge detection. This method improved the CT brain image and the gyri and sulci can be easily identified

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%

Determining the multi-current sources of magnetoencephalography by using Fuzzy Topographic Topological Mapping

The human brain is an extremely complex system performing demanding information processing tasks rapidly. It consists of billions of neurons, each connected to others through thousands of synapses or interconnections. This huge network has many electric and chemical processes that can be measured in various ways. Magnetoencephalography (MEG) is a technique of measuring and recording the minute and very weak magnetic fields generated by the currents in the neurons. There are two types of problems in MEG, the forward problem and the backward or the inverse problem. The forward problem deals with finding the magnetic fields when the current source distribution is given or known. On the other hand, the inverse problem is to find the neural current source distribution given a series of magnetic fields measurements. This study has proposed the model FTTM2 (Fuzzy Topographic Topological Mapping Version 2) which is an extension to the novel mathematical modeling FTTM1 (Fuzzy Topographic Topological Mapping Version 1). The model FTTM2 comprises four components namely the Image Contour Plane (IC), Base Image Plane (BI), Fuzzy Image Field (FI) and Topographic Image Field (TI). In the process of applying FTTM2, emphasis is made on its first component, the IC where two different algorithms are being applied to the data. The first is the fuzzy c-means (FCM) algorithm which is used to determine the region where the current sources lie and also to approximate the number of current sources. The second is the seed-based region growing (SBRG) algorithm which is used to confirm the number of current sources available in the system by automation. Two theorems and three corollaries are derived and proven as theoretical basis of the proposed system. Finally, FTTM2 is tested on the generated and experimental data and subsequently verified using forward and backward calculation

Homeomorphisms of Fuzzy Topographic Topological Mapping(FTTM)

The mathematical structure of a novel model for detecting the source of a magnetic field is presented. Simulation results show that the orientation of unbounded single current source can be determined with fairly accuracy. The model, FTTM, has the potential to be used for identifying the location of epileptic foci in epilepsy disorder patients

Segmentation of masses from breast ultrasound images using parametric active contour algorithm

The active contour (Snake) is a computer generated curve that can trace boundaries of images. As a method which applies the computer technology in mathematics, Snake is computationally formulated based on controlled continuous splines and adopts the mathematical concept of energy minimization. This paper presents the application of Snake for the segmentation of masses on breast ultrasound images. The images used are taken from Malaysian population. The boundaries of the masses identified may be used in classification of cancers or non-cancerous masses. Specifically the Balloon Snake is applied in segmenting the masses in the breast ultrasound images. Comparison on the masses areas segmented by the Balloon Snake is done against the areas traced by an expert (radiologist). It is found that from forty-five masses tested, the average percentage area difference of Balloon Snake is 4.47%. This implies that the accuracy of segmentation results for the Balloon Snake is 95.53%

Region and boundary segmentation of microcalcifications using seed-based region growing and mathematical morphology

In this paper an image segmentation technique is presented by combining seed based region growing and boundary segmentation in sequential order. The first process in region growing is to identify an initial seed point. Most of region growing methods identify the seed point manually which involve human interaction. Thus, automated initial seed point identification for region growing algorithm is proposed. The boundary segmentation technique is implemented in order to improve the segmentation results. The method is tested on 50 mammogram images confirmed by a radiologist to consist microcalcifications. Experimental results show that the algorithm successfully segment the microcalcifications with accuracy of 0.94