Review on Region-Based Segmentation Using Watershed and Region Growing Techniques and their Applications in Different Fields

In digital image processing and computer vision, segmentation operation for an image refers to dividing an image into multiple image segments, and the significant purpose of segmentation operation is to depict an image in a way so that the analysis process of the objects of interest is easier and more accurate. The region-based segmentation scheme act for finding similarities between adjacent pixels to detect each region that constructs the image. Similarity scales have based on different features, in a grayscale image, the scale may be referred to as textures and other spatial appearances, and also the variance in intensity of a region and so on. Significantly, many applications in different fields involved region-based segmentation for instance remote sensing, medical application, and others for recognizing interesting objects in an image. In this paper, two techniques for segmentation operation in region-based which are region growing and watershed are reviewed

An improved fast scanning algorithm based on distance measure and threshold function in region image segmentation

Segmentation is an essential and important process that separates an image into regions that have similar characteristics or features. This will transform the image for a better image analysis and evaluation. An important benefit of segmentation is the identification of region of interest in a particular image. Various algorithms have been proposed for image segmentation and this includes the Fast Scanning algorithm which has been employed on food, sport and medical image segmentation. The clustering process in Fast Scanning algorithm is performed by merging pixels with similar neighbor based on an identified threshold and the use of Euclidean Distance as distance measure. Such an approach leads to a weak reliability and shape matching of the produced segments. Hence, this study proposes an Improved Fast Scanning algorithm that is based on Sorensen distance measure and adaptive threshold function. The proposed adaptive threshold function is based on the grey value in an image’s pixels and variance. The proposed Improved Fast Scanning algorithm is realized on two datasets which contains images of cars and nature. Evaluation is made by calculating the Peak Signal to Noise Ratio (PSNR) for the Improved Fast Scanning and standard Fast Scanning algorithm. Experimental results showed that proposed algorithm produced higher PSNR compared to the standard Fast Scanning. Such a result indicate that the proposed Improved Fast Scanning algorithm is useful in image segmentation and later contribute in identifying region of interesting in pattern recognition

A COMPUTATIONAL PIPELINE FOR MCI DETECTION FROM HETEROGENEOUS BRAIN IMAGES

The aging population has increased the importance of identifying and understanding mild cognitive impairment (MCI), particularly given that 6 - 15 % of MCI cases convert to Alzheimer\u27s disease (AD) each year. The early identification of MCI has the potential for timely therapeutic interventions that would limit the advancement of MCI to AD. However, it is difficult to identify MCI-related pathology based on visual inspection because these changes in brain morphology are subtle and spatially distributed. Therefore, reliable and automated methods to identify subtle changes in morphological characteristics of MCI would aid in the identification and understanding of MCI. Meanwhile, usability becomes a major limitation in the development of clinically applicable classifiers. Furthermore, subject privacy is an additional issue in the usage of human brain images. To address the critical need, a complete computer aided diagnosis (CAD) system for automated detection of MCI from heterogeneous brain images is developed. This system provides functions for image processing, classification of MCI subjects from control, visualization of affected regions of interest (ROIs), data sharing among different research sites, and knowledge sharing through image annotation

A review of algorithms for medical image segmentation and their applications to the female pelvic cavity

This paper aims to make a review on the current segmentation algorithms used for medical images. Algorithms are classified according to their principal methodologies, namely the ones based on thresholds, the ones based on clustering techniques and the ones based on deformable models. The last type is focused on due to the intensive investigations into the deformable models that have been done in the last few decades. Typical algorithms of each type are discussed and the main ideas, application fields, advantages and disadvantages of each type are summarised. Experiments that apply these algorithms to segment the organs and tissues of the female pelvic cavity are presented to further illustrate their distinct characteristics. In the end, the main guidelines that should be considered for designing the segmentation algorithms of the pelvic cavity are proposed

Understanding Ocean Surface Temperature Features

The aim of this project was to develop a prediction system that uses Artificial Intelligence, machine learning using training data and Image Processing (AI) to extract training data from Sea Surface temperature (SST) images to predict the ocean surface, temperature features around the coast of the Southern African region. Region growing and histographic algorithms were used in the image processing section to extract thermal fronts as training data from the available SST images. A Temporal Bayesian Network was developed as the prediction model which used approximate stochastic learning and inference algorithms based on the Maximum Likelihood Algorithm (MLE). User-Centered Design (UCD) and Human-Computer Interaction (HCI) methods were used to develop user-friendly and easy to understand Graphical User Interfaces (GUI). Results and evaluations of the project revealed that a generally successful prototype implementation of a prediction system that used AI, machine learning and image processing was developed

Segmentation of cell structures in fluorescence confocal microscopy images

During the past several years, image segmentation techniques have been developed and extensively used in biomedical applications as an important tool to extract objects and boundaries of interest. In biological field, cytoskeleton analysis is a complicated problem and the analysing technique is still immature. Cytoskeleton plays an important role in normal cell activities, including motion and division, which make the cell cytoskeleton important to investigate. The objective of this project is to investigate and evaluate level set segmentation methods for segmentation of both cell nuclei and membrane segmentation of microfilament images captured by fluorescent confocal microscopy. Based on some background investigations, the active contour methodology has been selected as the fundamental method for image segmentation. This thesis presents the methods used and reports on the results achieved for cell and nuclei segmentation using the hybrid level-set method and cell membrane segmentation using the subjective surfaces model. In addition, some initial results of nuclei segmentation in 3-D case based on the hybrid method will be presented as well. Also included in this thesis are the method and the initial categorisation of microtubule images based on the multi-template method. At the end of the thesis, possible directions for potential future work are presented. It is envisaged that the segmentation tools produced by the project will make cell cytoskeleton data analysis much more convenient. In particular, the segmentation of cell membranes will help biologists to perform quantitative analysis of fluorescent confocal microscopy images by measuring the cell properties. With more useful information of cytoskeleton being provided, the work contained in this thesis has the potential to contribute to evaluation and prediction of the possibility of cell canceration

A Review of Left Ventricular Myocardium Analysis and Diagnosis Techniques for CT Images of Heart

Abstract Cardiovascular diseases associated with the left ventricle are main reason of deaths in heart diseases. Early diagnosis using advanced technologies will definitely aid in saving many lives. Cardiac computed tomography (CT) images are one of the tools for this function. Automatic segmentation of left ventricular myocardium is carried out from cardiac CT images. The system uses a iterative strategy for localization of left ventricle followed by deformation of myocardial surface to obtain refine segmentation i.e. blood pool surface of the CT image is extracted and triangulated surface is taken as an area of interest. Geometric characterization of triangulated surface gave precise localization of left ventricle. Subsequently, initialization of epicardial and endocacardial masks is done and myocardial wall is extracted. This paper gives review of different techniques used for segmentation revealed in previously reported literature along with the proposed technology. The proposed system is expected to work based on the standard rules defined by medical experts for disease diagnosis are yet to define