M3G: Maximum Margin Microarray Gridding

<p>Abstract</p> <p>Background</p> <p>Complementary DNA (cDNA) microarrays are a well established technology for studying gene expression. A microarray image is obtained by laser scanning a hybridized cDNA microarray, which consists of thousands of spots representing chains of cDNA sequences, arranged in a two-dimensional array. The separation of the spots into distinct cells is widely known as microarray image gridding.</p> <p>Methods</p> <p>In this paper we propose M³G, a novel method for automatic gridding of cDNA microarray images based on the maximization of the margin between the rows and the columns of the spots. Initially the microarray image rotation is estimated and then a pre-processing algorithm is applied for a rough spot detection. In order to diminish the effect of artefacts, only a subset of the detected spots is selected by matching the distribution of the spot sizes to the normal distribution. Then, a set of grid lines is placed on the image in order to separate each pair of consecutive rows and columns of the selected spots. The optimal positioning of the lines is determined by maximizing the margin between these rows and columns by using a maximum margin linear classifier, effectively facilitating the localization of the spots.</p> <p>Results</p> <p>The experimental evaluation was based on a reference set of microarray images containing more than two million spots in total. The results show that M³G outperforms state of the art methods, demonstrating robustness in the presence of noise and artefacts. More than 98% of the spots reside completely inside their respective grid cells, whereas the mean distance between the spot center and the grid cell center is 1.2 pixels.</p> <p>Conclusions</p> <p>The proposed method performs highly accurate gridding in the presence of noise and artefacts, while taking into account the input image rotation. Thus, it provides the potential of achieving perfect gridding for the vast majority of the spots.</p

KID Project:an internet-based digital video atlas of capsule endoscopy for research purposes

BACKGROUND AND AIMS: Capsule endoscopy (CE) has revolutionized small-bowel (SB) investigation. Computational methods can enhance diagnostic yield (DY); however, incorporating machine learning algorithms (MLAs) into CE reading is difficult as large amounts of image annotations are required for training. Current databases lack graphic annotations of pathologies and cannot be used. A novel database, KID, aims to provide a reference for research and development of medical decision support systems (MDSS) for CE. METHODS: Open-source software was used for the KID database. Clinicians contribute anonymized, annotated CE images and videos. Graphic annotations are supported by an open-access annotation tool (Ratsnake). We detail an experiment based on the KID database, examining differences in SB lesion measurement between human readers and a MLA. The Jaccard Index (JI) was used to evaluate similarity between annotations by the MLA and human readers. RESULTS: The MLA performed best in measuring lymphangiectasias with a JI of 81\u200a\ub1\u200a6\u200a%. The other lesion types were: angioectasias (JI 64\u200a\ub1\u200a11\u200a%), aphthae (JI 64\u200a\ub1\u200a8\u200a%), chylous cysts (JI 70\u200a\ub1\u200a14\u200a%), polypoid lesions (JI 75\u200a\ub1\u200a21\u200a%), and ulcers (JI 56\u200a\ub1\u200a9\u200a%). CONCLUSION: MLA can perform as well as human readers in the measurement of SB angioectasias in white light (WL). Automated lesion measurement is therefore feasible. KID is currently the only open-source CE database developed specifically to aid development of MDSS. Our experiment demonstrates this potential

Roadmap on signal processing for next generation measurement systems

Signal processing is a fundamental component of almost any sensor-enabled system, with a wide range of applications across different scientific disciplines. Time series data, images, and video sequences comprise representative forms of signals that can be enhanced and analysed for information extraction and quantification. The recent advances in artificial intelligence and machine learning are shifting the research attention towards intelligent, data-driven, signal processing. This roadmap presents a critical overview of the state-of-the-art methods and applications aiming to highlight future challenges and research opportunities towards next generation measurement systems. It covers a broad spectrum of topics ranging from basic to industrial research, organized in concise thematic sections that reflect the trends and the impacts of current and future developments per research field. Furthermore, it offers guidance to researchers and funding agencies in identifying new prospects.AerodynamicsMicrowave Sensing, Signals & System

Sensors, Signal and Image Processing in Biomedicine and Assisted Living

Sensor technologies are crucial in biomedicine, as the biomedical systems and devices used for screening and diagnosis rely on their efficiency and effectiveness [...

Software Engineering Applications in Gastroenterology

Abstract: Software engineering enables the construction of reliable services, which in the field of medicine can be regarded as crucial components of today’s clinical practice. This paper focuses on software engineering applications in the field of gastroenterology and presents the state of the art in this field as well as challenges for future research. It reviews a broad spectrum of applications with emphasis on endoscopic imaging. The advances in the latter are dominant, with hundreds of scientific contributions in the last quinquennium to be driven mainly by the technological revolution of wireless capsule endoscopy (WCE). However, there is still a long way for research before resolving the related open issues in practice

Software Engineering Applications in Gastroenterology

Software engineering enables the construction of reliable services, which in the field of medicine can be regarded as crucial components of today’s clinical practice. This paper focuses on software engineering applications in the field of gastroenterology and presents the state of the art in this field as well as challenges for future research. It reviews a broad spectrum of applications with emphasis on endoscopic imaging. The advances in the latter are dominant, with hundreds of scientific contributions in the last quinquennium to be driven mainly by the technological revolution of wireless capsule endoscopy (WCE). However, there is still a long way for research before resolving the related open issues in practice