Optimization based Tuberculosis Image Segmentation by Ant Colony Heuristic Method

Tuberculosis (TB) is a worldwide health crisis and is the second primary infectious disease that causes death next to human immunodeficiency virus. In this work, an attempt has been made to detect the presence of bacilli in sputum smears. The smear images recorded under standard image acquisition protocol are subjected to hybrid Ant Colony Optimization (ACO)-morphological based segmentation procedure. This method is able to retain the shape of bacilli in TB images. The segmented images are validated with ground truth using overlap, distance and probability-based measures. Significant shape-based features such as area, perimeter, compactness, shape factor and tortuosity are extracted from the segmented images. It is observed that this method preserves more edges, detects the presence of bacilli and facilitates direct segmentation with reduced number of redundant searches to generate edges. Thus this hybrid segmentation technique aid in the diagnostic relevance of TB images in identifying the objects present in them.

Optimization based Tuberculosis Image Segmentation by Ant Colony Heuristic Method

Tuberculosis (TB) is a worldwide health crisis and is the second primary infectious disease that causes death next to human immunodeficiency virus. In this work, an attempt has been made to detect the presence of bacilli in sputum smears. The smear images recorded under standard image acquisition protocol are subjected to hybrid Ant Colony Optimization (ACO)-morphological based segmentation procedure. This method is able to retain the shape of bacilli in TB images. The segmented images are validated with ground truth using overlap, distance and probability-based measures. Significant shape-based features such as area, perimeter, compactness, shape factor and tortuosity are extracted from the segmented images. It is observed that this method preserves more edges, detects the presence of bacilli and facilitates direct segmentation with reduced number of redundant searches to generate edges. Thus this hybrid segmentation technique aid in the diagnostic relevance of TB images in identifying the objects present in them.

DiaMe: IoMT deep predictive model based on threshold aware region growing technique

Medical images magnetic resonance imaging (MRI) analysis is a very challenging domain especially in the segmentation process for predicting tumefactions with high accuracy. Although deep learning techniques achieve remarkable success in classification and segmentation phases, it remains a rich area to investigate, due to the variance of tumefactions sizes, locations and shapes. Moreover, the high fusion between tumors and their anatomical appearance causes an imprecise detection for tumor boundaries. So, using hybrid segmentation technique will strengthen the reliability and generality of the diagnostic model. This paper presents an automated hybrid segmentation approach combined with convolution neural network (CNN) model for brain tumor detection and prediction, as one of many offered functions by the previously introduced IoMT medical service “DiaMe”. The developed model aims to improve extracting region of interest (ROI), especially with the variation sizes of tumor and its locations; and hence improve the overall performance of detecting the tumor. The MRI brain tumor dataset obtained from Kaggle, where all needed augmentation, edge detection, contouring and binarization are presented. The results showed 97.32% accuracy for detection, 96.5% Sensitivity, and 94.8% for specificity.

USING A NEW HYBRID SEGMENTATION TECHNIQUE TO SEGMENT LUNAR CRATERS AND DETERMINE ITS DIAMETER

Segmentation method is the process of partition digital image into parts depending to color, texture, and intensity. There are many segmentation methods used in different fields according to the purpose of application. In this study the global thresholding and proposed hybrid method were used to segment lunar craters. Craters on Moon's surface caused by collision between Moon and celestial objects as comet, meteorite, asteroids and others. Due to the Moon has no atmosphere, the lunar surface covered by a huge number of craters different in their size and depth depending on velocity and size of collided objects. The study of lunar craters provide information about the age and geology of a Moon's surface. So, we proposed a novel hybrid segmentation method to segment Moon's images and isolate lunar craters from other surface features and then determine the diameter of lunar craters. The proposed hybrid method combine the performance of K-Means and SFFNN together. The results shown that, the proposed method gives very acceptable outcome, where the boundaries of lunar craters were delineate in professional way that lead to accurate determination of its diameters.

Psycho-Behavioural Segmentation in Food and Nutrition: A Systematic Scoping Review of the Literature

Inadequate dietary intakes are a key modifiable risk factor to reduce the risk of developing non-communicable diseases. To encourage healthy eating and behaviour change, innovative public health interventions are required. Social marketing, in particular segmentation, can be used to understand and target specific population groups. However, segmentation often uses demographic factors, ignoring the reasons behind why people behave the way they do. This review aims to explore the food and nutrition related research that has utilised psycho-behavioural segmentation. Six databases from were searched in June 2020. Inclusion criteria were: published 2010 onwards, segmentation by psycho-behavioural variables, outcome related to food or nutrition, and healthy adult population over 18 years. 30 studies were included; most were quantitative (n = 28) and all studies used post-hoc segmentation methods, with the tools used to segment the population varying. None of the segments generated were targeted in future research. Psycho-behavioural factors are key in understanding people’s behaviour. However, when used in post-hoc segmentation, do not allow for effective targeting as there is no prior understanding of behaviours that need to change within each segment. In future, we should move towards hybrid segmentation to assist with the design of interventions that target behaviours such as healthy eating.

A Fast, Fully Automated Prostate Boundary Segmentation Using Probabilistic Approaches In Ultrasound Images

Segmentation of prostate boundaries in transrectal ultrasound (TRUS) images plays a great role in prostate cancer diagnosis. Due to the low signal to noise ratio and existence of the speckle noise in TRUS images, prostate image segmentation has proven to be an extremely difficult task. In this thesis report, a fast fully automated hybrid segmentation method based on probabilistic approaches is presented. First, the position of the initial model is automatically estimated using prostate boundary representative patterns. Next, the Expectation Maximization (EM) algorithm and Markov Random Field (MRF) theory are utilized in the deformation strategy to optimally fit the initial model on the prostate boundaries. A less computationally EM algorithm and a new surface smoothing technique are proposed to decrease the segmentation time. Successful experimental results with the average Dice Similarity Coefficient (DSC) value 93.9±2.7% and computational time around 9 seconds validate the algorithm.

A Fast, Fully Automated Prostate Boundary Segmentation Using Probabilistic Approaches In Ultrasound Images

Segmentation of prostate boundaries in transrectal ultrasound (TRUS) images plays a great role in prostate cancer diagnosis. Due to the low signal to noise ratio and existence of the speckle noise in TRUS images, prostate image segmentation has proven to be an extremely difficult task. In this thesis report, a fast fully automated hybrid segmentation method based on probabilistic approaches is presented. First, the position of the initial model is automatically estimated using prostate boundary representative patterns. Next, the Expectation Maximization (EM) algorithm and Markov Random Field (MRF) theory are utilized in the deformation strategy to optimally fit the initial model on the prostate boundaries. A less computationally EM algorithm and a new surface smoothing technique are proposed to decrease the segmentation time. Successful experimental results with the average Dice Similarity Coefficient (DSC) value 93.9±2.7% and computational time around 9 seconds validate the algorithm.