Umi Kalthum Ngah

Adaptive fuzzy moving K-means clustering algorithm for image segmentation

Image segmentation remains one of the major challenges in image analysis. Many segmentation algorithms have been developed for various applications. Unsatisfactory results have been encountered in some cases, for many existing segmentation algorithms. In this paper, we introduce three modified versions of the conventional moving k-means clustering algorithm called the fuzzy moving k-means, adaptive moving k-means and adaptive fuzzy moving k-means algorithms for image segmentation application. Based on analysis done using standard images (i.e. original bridge and noisy bridge) and hard evidence on microscopic digital image (i.e. segmentation of Sprague Dawley rat sperm), our final segmentation results compare favorably with the results obtained by the conventional k-means, fuzzy c-means and moving k-means algorithms. The qualitative and quantitative analysis done proved that the proposed algorithms are less sensitive with respect to noise. As such, the occurrence of dead centers, center redundancy and trapped center at local minima problems can be avoided. The proposed clustering algorithms are also less sensitive to initialization process of clustering value. The final center values obtained are located within their respective groups of data. This enabled the size and shape of the object in question to be maintained and preserved. Based on the simplicity and capabilities of the proposed algorithms, these algorithms are suitable to be implemented in consumer electronics products such as digital microscope, or digital camera as post processing tool for digital images.

Modeling and Testing Landslide Hazard Using Decision Tree

This paper proposes a decision tree model for specifying the importance of 21 factors causing the landslides in a wide area of Penang Island, Malaysia. These factors are vegetation cover, distance from the fault line, slope angle, cross curvature, slope aspect, distance from road, geology, diagonal length, longitude curvature, rugosity, plan curvature, elevation, rain perception, soil texture, surface area, distance from drainage, roughness, land cover, general curvature, tangent curvature, and profile curvature. Decision tree models are used for prediction, classification, and factors importance and are usually represented by an easy to interpret tree like structure. Four models were created using Chi-square Automatic Interaction Detector (CHAID), Exhaustive CHAID, Classification and Regression Tree (CRT), and Quick-Unbiased-Efficient Statistical Tree (QUEST). Twenty-one factors were extracted using digital elevation models (DEMs) and then used as input variables for the models. A data set of 137570 samples was selected for each variable in the analysis, where 68786 samples represent landslides and 68786 samples represent no landslides. 10-fold cross-validation was employed for testing the models. The highest accuracy was achieved using Exhaustive CHAID (82.0%) compared to CHAID (81.9%), CRT (75.6%), and QUEST (74.0%) model. Across the four models, five factors were identified as most important factors which are slope angle, distance from drainage, surface area, slope aspect, and cross curvature.

Image classification of brain MRI using support vector machine

One of the primary diagnostic and treatment evaluation tools for brain interpretation has been magnetic resonance imaging (MRI). It has been a widely-used method of high quality medical imaging, especially in brain imaging where MRs soft tissue contrast and non invasiveness are clear advantages. MR images can also be used to determine a normal and abnormal types of brain. Moreover, the MRI characteristics will help the doctor to avoid the human error in manual interpretation of medical content. Computer-based classification has remained largely experimental work with approaches, one of them is, Support vector machine (SVM). SVM is a pattern recognition algorithm which learns to assign labels to objects through examples. This research paper is an attempt to use SVM to automatically classify brain MRI images under two categories, either normal or abnormal brain which refers to brain tumor. The determination of normal and abnormal brain image is based on symmetry which is exhibited in the axial and coronal images. Using feature vector gained from the MRI images, SVM classifiers are use to classify the images. The process consists of two components which are training phase and a testing phase. Percentage of accuracy on each parameter in SVM will give the idea to choose the best one to be used in further works. Other than that, value of percentage will give the first interpretation either the brain image has the possibility of brain tumor or normal. After all, we are using LabView Advanced Signal Processing Toolkit as the software in our experimental work. We believe with the easiness of this graphical programming and the capabilities of SVM will give a very good result.

Seed-based region growing study for brain abnormalities segmentation

This paper proposes an empirical study of the efficiency of the Seed-Based Region Growing (SBRG) in segmentation of brain abnormalities. Presently, segmentation poses one of the most challenging problems in medical imaging. Segmentation of Magnetic Resonance Imaging (MRI) images is an important part of brain imaging research. In this paper, we used controlled experimental data as our testing data. The data is designed in such a way that prior knowledge of the size of the abnormalities are known. This is done by cutting various shapes and sizes of various abnormalities and pasting it onto normal brain tissues, where the tissues and the background are divided into different categories. The segmentation was done with twenty data of each category. The knowledge of the size of the abnormalities by the number of pixels were then used as the ground truth to compare with the SBRG segmentation results. The proposed SBRG technique was found to produce potential solutions to the current difficulties in detecting abnormalities in the human brain tissue area.

Computer-Aided Segmentation System for Breast MRI Tumour using Modified Automatic Seeded Region Growing (BMRI-MASRG)

In this paper, an automatic computer-aided detection system for breast magnetic resonance imaging (MRI) tumour segmentation will be presented. The study is focused on tumour segmentation using the modified automatic seeded region growing algorithm with a variation of the automated initial seed and threshold selection methodologies. Prior to that, some pre-processing methodologies are involved. Breast skin is detected and deleted using the integration of two algorithms, namely the level set active contour and morphological thinning. The system is applied and tested on 40 test images from the RIDER breast MRI dataset, the results are evaluated and presented in comparison to the ground truths of the dataset. The analysis of variance (ANOVA) test shows that there is a statistically significance in the performance compared to the previous segmentation approaches that have been tested on the same dataset where ANOVA p values for the evaluation measures’ results are less than 0.05, such as: relative overlap (p = 0.0002), misclassification rate (p = 0.045), true negative fraction (p = 0.0001) and sum of true volume fraction (p = 0.0001).

Improvement of MRI brain classification using principal component analysis

The field of medical imaging gains its importance with increase in the need of automated and efficient diagnosis in a short period of time. One of the primary diagnostic and treatment evaluation tools for brain interpretation has been magnetic resonance imaging (MRI). It has been a widely-used method of high quality medical imaging, especially in brain imaging where MRIs soft tissue contrast and non invasiveness are clear advantages. Classification is an important part in retrieval system. The classifications of brain MRI data as normal and abnormal are important to prune the normal patient and to consider only those who have the possibility of having abnormalities or tumor. This step was done by using support vector machine (SVM). The aim of this paper is to compare percentage of accuracy in classification data with and without the implementation of principal component analysis (PCA). As a result, we found that by using PCA method, the number of feature vector has been reduced from 17689 to 200 and increase the percentage of accuracy.

Density based breast segmentation for mammograms using graph cut techniques

In this work we explore the application of graph cuts techniques to the problem of finding the boundary of different breast tissue regions in mammograms. The goal of the segmentation algorithm is to see if graph cuts algorithm could separate different densities for the different breast patterns. The graph cut is applied with multi-selection of seeds label to provide the hard constraint, whereas the seeds labels are selected based on user defined. Graph cuts have been explored on images of various imaging modalities but not on mammograms just yet. Therefore, this project is mainly focused on using graph cut algorithm to perform segmentation to increase visibility of different breast densities in mammography images. Segmentation of the mammogram into different mammographic densities is useful for risk assessment and quantitative evaluation of density changes. Our proposed methodology for the segmentation of mammograms on the basis of their region into different densities based categories has been tested on MIAS database.

Efficient Hardware-Based Image Compression Schemes for Wireless Sensor Networks: A Survey

Multidimensional sensors, such as digital camera sensors in the visual sensor networks VSNs generate a huge amount of information compared with the scalar sensors in the wireless sensor networks WSNs. Processing and transmitting such data from low power sensor nodes is a challenging issue through their limited computational and restricted bandwidth requirements in a hardware constrained environment. Source coding can be used to reduce the size of vision data collected by the sensor nodes before sending it to its destination. With image compression, a more efficient method of processing and transmission can be obtained by removing the redundant information from the captured image raw data. In this paper, a survey of the main types of the conventional state of the art image compression standards such as JPEG and JPEG2000 is provided. A literature review of their advantages and shortcomings of the application of these algorithms in the VSN hardware environment is specified. Moreover, the main factors influencing the design of compression algorithms in the context of VSN are presented. The selected compression algorithm may have some hardware-oriented properties such as; simplicity in coding, low memory need, low computational load, and high-compression rate. In this survey paper, an energy efficient hardware based image compression is highly requested to counter the severe hardware constraints in the WSNs.

Determination of Important Topographic Factors for Landslide Mapping Analysis Using MLP Network

Landslide is one of the natural disasters that occur in Malaysia. Topographic factors such as elevation, slope angle, slope aspect, general curvature, plan curvature, and profile curvature are considered as the main causes of landslides. In order to determine the dominant topographic factors in landslide mapping analysis, a study was conducted and presented in this paper. There are three main stages involved in this study. The first stage is the extraction of extra topographic factors. Previous landslide studies had identified mainly six topographic factors. Seven new additional factors have been proposed in this study. They are longitude curvature, tangential curvature, cross section curvature, surface area, diagonal line length, surface roughness, and rugosity. The second stage is the specification of the weight of each factor using two methods. The methods are multilayer perceptron (MLP) network classification accuracy and Zhous algorithm. At the third stage, the factors with higher weights were used to improve the MLP performance. Out of the thirteen factors, eight factors were considered as important factors, which are surface area, longitude curvature, diagonal length, slope angle, elevation, slope aspect, rugosity, and profile curvature. The classification accuracy of multilayer perceptron neural network has increased by 3% after the elimination of five less important factors.

Computer Aided Detection of Breast Density and Mass, and Visualization of Other Breast Anatomical Regions on Mammograms Using Graph Cuts

Breast cancer mostly arises from the glandular (dense) region of the breast. Consequently, breast density has been found to be a strong indicator for breast cancer risk. Therefore, there is a need to develop a system which can segment or classify dense breast areas. In a dense breast, the sensitivity of mammography for the early detection of breast cancer is reduced. It is difficult to detect a mass in a breast that is dense. Therefore, a computerized method to separate the existence of a mass from the glandular tissues becomes an important task. Moreover, if the segmentation results provide more precise demarcation enabling the visualization of the breast anatomical regions, it could also assist in the detection of architectural distortion or asymmetry. This study attempts to segment the dense areas of the breast and the existence of a mass and to visualize other breast regions (skin-air interface, uncompressed fat, compressed fat, and glandular) in a system. The graph cuts (GC) segmentation technique is proposed. Multiselection of seed labels has been chosen to provide the hard constraint for segmentation of the different parts. The results are promising. A strong correlation (r = 0.93) was observed between the segmented dense breast areas detected and radiological ground truth.