2015英特爾ISEF獲獎(jiǎng)作品摘要 計(jì)算生物與生物信息學(xué) Intel Finalist Abstract Computational Biology and Bioinformatics

獲獎(jiǎng)作品基本信息

年份	2015
學(xué)科	計(jì)算生物與生物信息學(xué) Computational Biology and Bioinformatics
國家/州	United States of America

獲獎(jiǎng)作品名稱

Automatic Detection of Vascular Lesions of the Retina Using a Localized Adaptive Thresholding Approach

獲獎(jiǎng)作品摘要

I created a software application that, when integrated with an optical addendum to smartphones, allows for mobile pre-screening for ocular diseases in almost any setting. Early detection of vascular lesions such as exudates, hemorrhages, and tumors in the retina is important for early diagnosis of diabetes, hypertension, and cancer.? Diabetic retinopathy (DR) is the leading cause of adult blindness in the United States, and the presence of exudates in fundus imagery (images of the retina) is an early sign of DR. Early detection of DR decreases the risk of severe vision loss by greater than 90%. I present a novel technique developed in MATLAB and adapted from radar imagery analysis to automatically detect lesions in fundus imagery that is robust against spatial and temporal variations of background noise. The detection threshold is adjusted dynamically based on the local noise statistics around the Pixel-Under-Test in order to maintain a pre-determined, constant false alarm rate (CFAR). A pre-processing step accommodates the detection of bright lesions (exudates) as well as dark lesions (hemorrhages and tumors). In this novel application to fundus imagery, the algorithm addresses the challenge of detecting lesions in color and multispectral fundus imagery where the background clutter often exhibits variations in brightness and texture. These variations present challenges to common, global thresholding detection algorithms. Performance of the adaptive-threshold CFAR algorithm is assessed using a publicly available, annotated, DR database. Performance of the CFAR detector is presented and proves to be superior to more common detection techniques such as Otsu thresholding.

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ISEF 簡介

英特爾國際科學(xué)與工程大獎(jiǎng)賽，簡稱 "ISEF"，由美國 Society for Science and the Public（科學(xué)和公共服務(wù)協(xié)會(huì)）主辦，英特爾公司冠名贊助，是全球規(guī)模最大、等級最高的中學(xué)生的科研科創(chuàng)賽事。ISEF 的學(xué)術(shù)活動(dòng)學(xué)科包括了所有數(shù)學(xué)、自然科學(xué)、工程的全部領(lǐng)域和部分社會(huì)科學(xué)。ISEF 素有全球青少年科學(xué)學(xué)術(shù)活動(dòng)的“世界杯”之美譽(yù)，旨在鼓勵(lì)學(xué)生團(tuán)隊(duì)協(xié)作，開拓創(chuàng)新，長期專一深入地研究自己感興趣的課題。

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英特爾 ISEF 學(xué)術(shù)活動(dòng)詳細(xì)介紹

英特爾 ISEF 全程指導(dǎo)方案

· 數(shù)學(xué) · 物理 · 化學(xué) · 生物 · 計(jì)算機(jī) · 工程 ·

學(xué)科簡介：計(jì)算生物與生物信息學(xué) Computational Biology and Bioinformatics

Studies that primarily focus on the discipline and techniques of computer science and mathematics as they relate to biological systems. This includes the development and application of data-analytical and theoretical methods, mathematical modeling and computational simulation techniques to the study of biological, behavior, and social systems.

Subcategories:

Computational Biomodeling?(MOD):?Studies that involve computer simulations of biological systems most commonly with a goal of understanding how cells or organism develop, work collectively and survive.

Computational Epidemiology (EPD):?The study of disease frequency and distribution, and risk factors and socioeconomic determinants of health within populations. Such studies may include gathering information to confirm existence of disease outbreaks, developing case definitions and analyzing epidemic data, establishing disease surveillance, and implementing methods of disease prevention and control.

Computational Evolutionary Biology?(EVO):?A study that applies the discipline and techniques of computer science and mathematics to explore the processes of change in populations of organisms, especially taxonomy, paleontology, ethology, population genetics and ecology.

Computational Neuroscience?(NEU):?A study that applies the discipline and techniques of computer science and mathematics to understand brain function in terms of the information processing properties of the structures that make up the nervous system.

Computational Pharmacology?(PHA):?A study that applies the discipline and techniques of computer science and mathematics to predict and analyze the responses to drugs.

Genomics?(GEN):?The study of the function and structure of genomes using recombinant DNA, sequencing, and bioinformatics.

Other?(OTH):?Studies that cannot be assigned to one of the above subcategories. If the project involves multiple subcategories, the principal subcategory should be chosen instead of Other.