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

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

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

Segmented hydrogels: process development of a reproducible 3D tissue engineered interface system and its use as a muscle–tendon model

Prior to commercialisation, all drugs and medical devices must undergo testing to ensure safety to the end user. Part of this process is the pre-clinical trials stage in which high-throughput testing of the product is performed on cells in monolayer followed by testing in animal models. Monolayer cultures are generally basic, containing one cell type, which leads to minimal testing parameters. The more complex animal tests are often misleading as they do not adequately represent the human physiology and their ethics are also often contested. 3D Tissue engineered models, an evolution of the monolayer model more accurately mimic the structure and biochemistry of specific native tissues. To observe effects on the musculoskeletal system, a model representing these tissues is necessary. This thesis focuses on attempting to create an in vitro myotendinous junction (MTJ) for such purposes. Firstly, the most suitable published process for making a 3D tissue engineered skeletal muscle model was identified based on an analysis of requirements. A model using the C2C12 cell line in a collagen hydrogel between two anchor points was chosen and the process was optimised using a Quality-by-Design framework. This was essential to make a system that would lend itself to high-throughput testing in the long run. Following this, a simple process for creating an MTJ, termed ‘segmentation’ of the gel, was tested and showed a reduction in surface area consistent with cell attachment as previously reported. This involved physically blocking regions of the gel during manufacture. Multiple design iterations were tested to enable reproducibility. Of the tested configurations, a 3D printed PLA mould adhered to a 6-well plate with sliding dividers for segmentation and posts for gel anchor points was found to be optimal. Finally, standardising the use of ice in the gel fabrication process to prevent premature polymerisation of the hydrogel led to the success rate of fabrication to increase to up to 100%. Comparisons with the initial system showed multiple indicators of more consistent gels with reduced failure rates, a reduction in the resources required due to scaling down, and versatility in the design allowing for segmentation and simple adaptation to testing apparatus for future experiments. This system was then tested by only seeding the central region of a gel with C2C12 muscle-precursor cells to create “segmented gels”. Compared to homogenously seeded constructs, the ‘muscle’ region in segmented gels was found to have no difference in macroscopic behaviour and only a slight decrease in myotube width measurements, still within published parameters. These models exhibited a unique ‘bow-tie’ shape from the seeding discrepancies in the different regions. During the 14-day culture period, the cells became equally distributed throughout the gel, indicating that they may be migrating over the culture period. These regions also exhibited myotube formation and although less densely populated, a greater incidence of striated myotubes were found in these regions as demonstrated by staining with rhodamine phalloidin. Finally, the end regions were seeded with human dermal fibroblasts (hDFs) to represent a tendon to create a tendon-muscle-tendon model. Immunostaining showed that the majority of cells in the resulting construct were desmin-positive, a muscle-specific marker. This is in agreement with previous research that shows that dermal fibroblasts can be driven down a myogenic lineage by secreted factors in culture. However, transitional interdigitation between the two morphologically different cell types were observed in some models. This represents the first report of the successful formation of a myotendinous junction in a collagen-based potentially high-throughput system

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

Fast extraction of neuron morphologies from large-scale SBFSEM image stacks

Neuron morphology is frequently used to classify cell-types in the mammalian cortex. Apart from the shape of the soma and the axonal projections, morphological classification is largely defined by the dendrites of a neuron and their subcellular compartments, referred to as dendritic spines. The dimensions of a neuron’s dendritic compartment, including its spines, is also a major determinant of the passive and active electrical excitability of dendrites. Furthermore, the dimensions of dendritic branches and spines change during postnatal development and, possibly, following some types of neuronal activity patterns, changes depending on the activity of a neuron. Due to their small size, accurate quantitation of spine number and structure is difficult to achieve (Larkman, J Comp Neurol 306:332, 1991). Here we follow an analysis approach using high-resolution EM techniques. Serial block-face scanning electron microscopy (SBFSEM) enables automated imaging of large specimen volumes at high resolution. The large data sets generated by this technique make manual reconstruction of neuronal structure laborious. Here we present NeuroStruct, a reconstruction environment developed for fast and automated analysis of large SBFSEM data sets containing individual stained neurons using optimized algorithms for CPU and GPU hardware. NeuroStruct is based on 3D operators and integrates image information from image stacks of individual neurons filled with biocytin and stained with osmium tetroxide. The focus of the presented work is the reconstruction of dendritic branches with detailed representation of spines. NeuroStruct delivers both a 3D surface model of the reconstructed structures and a 1D geometrical model corresponding to the skeleton of the reconstructed structures. Both representations are a prerequisite for analysis of morphological characteristics and simulation signalling within a neuron that capture the influence of spines