34 research outputs found
Intelligent System for High Resolution Computed Tomography (HRCT) Image Analysis: A concept
No full textFurthermore, a large collection of datasets from numerous of medical experts, will pave the way to perform datamining and discover correlative effects of some of the diseases with either the artifacts present in the images or the demo graphical data of the patients. Therefore it can be concluded that the future positive ramifications of such a system is extensive and crucial to the well-being of the population
Intelligent System for High Resolution Computed Tomography (HRCT) Image Analysis: A concept
No full textFurthermore, a large collection of datasets from numerous of medical experts, will pave the way to perform datamining and discover correlative effects of some of the diseases with either the artifacts present in the images or the demo graphical data of the patients. Therefore it can be concluded that the future positive ramifications of such a system is extensive and crucial to the well-being of the population
Intelligent System for High Resolution Computed Tomography (HRCT) Image Analysis: A concept
No full textFurthermore, a large collection of datasets from numerous of medical experts, will pave the way to perform datamining and discover correlative effects of some of the diseases with either the artifacts present in the images or the demo graphical data of the patients. Therefore it can be concluded that the future positive ramifications of such a system is extensive and crucial to the well-being of the population
Matlab code for "Centromere detection of human metaphase chromosome images using a candidate based method"
No full text<p>Matlab code files used in analysing the chromosome images</p>
Chromosome images used for "Centromere detection of human metaphase chromosome images using a candidate based method"
No full textChromosome image data used in the paper "Centromere detection of human metaphase chromosome images using a candidate based method". Images are in tiff forma
Automated human chromosome segmentation and feature extraction: Current trends and prospects
No full textAutomated human chromosome segmentation and feature extraction aim to improve the overall quality of genetic disorder diagnosis by addressing the limitations of tedious manual processes such as expertise dependence, time-inefficiency, observer variability and fatigue errors. Nevertheless, significant differences caused by staining methods, chromosome damage which may occur during imaging, cell and staining debris, inhomogeneity, weak boundaries, morphological variations, premature sister chromatid separation, as well as the presence of overlapping, touching, di-centric and bent chromosomes pose challenges in automated human chromosome segmentation and feature extraction. This review paper extensively discusses how the approaches presented in literature have addressed these challenges, and their strengths and limitations. Human chromosome segmentation algorithms are presented under four broad categories; thresholding, clustering, active contours and convex-concave points-based methods. Chromosome feature extraction methods are discussed under two main categories based on banding-pattern and geometry. In addition, new insights for the improvement of fully automated karyotyping are provided
Efficient Medical Video Streaming by Pre-Processing and Network Traffic Prioritization in Real-Time
No full textDeveloping advanced healthcare applications to cater to the requirements of an ever-growing population has become one of the key areas of research in engineering. One major application in this area is medical video streaming, which is often used for remote monitoring of patients. Medical video streaming helps to overcome geographical barriers and offers medical services at the convenience of the patient. However, as medical videos carry critical and time-sensitive information, retaining the quality and reducing latency during transmission is paramount for accurate medical diagnosis. This paper presents the concept of effective medical video streaming, which incorporates novel methods in video pre-processing, video compression, and transmission of medical data over optical networks.<br/
The evolution of ChatGPT for programming:a comparative study
No full textThe introduction of Transformer models has significantly advanced natural language processing, with the development of Large Language Models (LLMs) like GPT-4 and Gemini revolutionizing industries by automating programming tasks. However, challenges remain regarding their ability to fully replace human programmers, especially in terms of efficiency and handling complex problems. This study aims to evaluate the performance of GPT models in solving algorithmic problems across three programming languages—Python, Java, and C++. It focuses on assessing runtime and memory efficiency to provide insights into the capabilities and limitations of LLMs in practical programming tasks. We selected 15 LeetCode problems categorized by difficulty and instructed GPT-3.5, GPT-4, and GPT-4o to generate solutions in Python, Java, and C++. Code was generated and executed 10 times for each problem, measuring runtime and memory usage. Statistical analyses, including two-way ANOVA and post hoc Tukey’s HSD tests, were conducted to evaluate the results. The findings indicate that programming language has a significant effect on memory and runtime efficiency, with C++ outperforming Python and Java. However, there were no statistically significant differences in performance between GPT-3.5, GPT-4, and GPT-4o across most tasks. Python was found to be significantly slower and more memory-intensive compared to C++ and Java. While GPT models show promise in assisting with programming tasks, their practical utility remains limited, particularly for complex problems. Improvements in newer GPT models do not always translate into significant performance gains. The choice of programming language plays a crucial role in optimizing LLM-generated code, suggesting that LLMs are better suited for augmenting human programmers rather than replacing them in critical tasks
Centromere detection of human metaphase chromosome images using a candidate based method
No full textAccurate detection of the human metaphase chromosome centromere is an critical element of cytogenetic diagnostic techniques, including chromosome enumeration, karyotyping and radiation biodosimetry. Existing image processing methods can perform poorly in the presence of irregular boundaries, shape variations and premature sister chromatid separation, which can adversely affect centromere localization. We present a centromere detection algorithm that uses a novel profile thickness measurement technique on irregular chromosome structures defined by contour partitioning. Our algorithm generates a set of centromere candidates which are then evaluated based on a set of features derived from images of chromosomes. Our method also partitions the chromosome contour to isolate its telomere regions and then detects and corrects for sister chromatid separation. When tested with a chromosome database consisting of 1400 chromosomes collected from 40 metaphase cell images, the candidate based centromere detection algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%. We also introduce a Candidate Based Centromere Confidence (CBCC) metric which indicates an approximate confidence value of a given centromere detection and can be readily extended into other candidate related detection problems.<br/
Centromere detection of human metaphase chromosome images using a candidate based method
No full textAccurate detection of the human metaphase chromosome centromere is an critical element of cytogenetic diagnostic techniques, including chromosome enumeration, karyotyping and radiation biodosimetry. Existing image processing methods can perform poorly in the presence of irregular boundaries, shape variations and premature sister chromatid separation, which can adversely affect centromere localization. We present a centromere detection algorithm that uses a novel profile thickness measurement technique on irregular chromosome structures defined by contour partitioning. Our algorithm generates a set of centromere candidates which are then evaluated based on a set of features derived from images of chromosomes. Our method also partitions the chromosome contour to isolate its telomere regions and then detects and corrects for sister chromatid separation. When tested with a chromosome database consisting of 1400 chromosomes collected from 40 metaphase cell images, the candidate based centromere detection algorithm was able to accurately localize 1220 centromere locations yielding a detection accuracy of 87%. We also introduce a Candidate Based Centromere Confidence (CBCC) metric which indicates an approximate confidence value of a given centromere detection and can be readily extended into other candidate related detection problems.<br/
