Mixture of learners for cancer stem cell detection using CD13 and H and e stained images

In this article, algorithms for cancer stem cell (CSC) detection in liver cancer tissue images are developed. Conventionally, a pathologist examines of cancer cell morphologies under microscope. Computer aided diagnosis systems (CAD) aims to help pathologists in this tedious and repetitive work. The first algorithm locates CSCs in CD13 stained liver tissue images. The method has also an online learning algorithm to improve the accuracy of detection. The second family of algorithms classify the cancer tissues stained with H and E which is clinically routine and cost effective than immunohistochemistry (IHC) procedure. The algorithms utilize 1D-SIFT and Eigen-Analysis based feature sets as descriptors. Normal and cancerous tissues can be classified with 92.1% accuracy in H and E stained images. Classification accuracy of low and high-grade cancerous tissue images is 70.4%. Therefore, this study paves the way for diagnosing the cancerous tissue and grading the level of it using H and E stained microscopic tissue images. © 2016 SPIE

Verfahren und Vorrichtung zum Zusammenfuegen von mehreren digitalen Einzelbildern zu einem Gesamtbild

WO 2011023657 A1 UPAB: 20110321 NOVELTY - The method involves determining multiple features in an individual image (I-a) by a selection unit (15) using a feature-based algorithm. The multiple features are subsequently tracked in another individual image (I-b) by a tracking unit (16). A transformation matrix is calculated from the determined feature in a transformation unit (17). DETAILED DESCRIPTION - An INDEPENDENT CLAIM is also included for an apparatus for joining multiple individual digital images into a total image, and for use in an endoscope system. USE - Method for joining multiple individual digital images into a total image. ADVANTAGE - The method enables joining multiple individual digital images in real time with high accuracy