WHIDE—a web tool for visual data mining colocation patterns in multivariate bioimages

Motivation: Bioimaging techniques rapidly develop toward higher resolution and dimension. The increase in dimension is achieved by different techniques such as multitag fluorescence imaging, Matrix Assisted Laser Desorption / Ionization (MALDI) imaging or Raman imaging, which record for each pixel an N-dimensional intensity array, representing local abundances of molecules, residues or interaction patterns. The analysis of such multivariate bioimages (MBIs) calls for new approaches to support users in the analysis of both feature domains: space (i.e. sample morphology) and molecular colocation or interaction. In this article, we present our approach WHIDE (Web-based Hyperbolic Image Data Explorer) that combines principles from computational learning, dimension reduction and visualization in a free web application

Apple Grade Inspection by Using Machine Vision

We see the world around through our eyes. Our eyes are the sensory organs that capture images and transmit to our brain at very fast rate. The image is representation of real scene either in black & white or in colour. The brain performs various processing functions and vision is perceived. In human beings we make use of vision for accomplishing majority of our tasks. Blindfolding ourselves and observing how our daily routine is seriously hampered without our vision can easily verify this fact. Although the first machine that captured image was a pinhole camera that was invented way back in 1850s, which was followed by many advances in image capturing techniques. Black & white camera gave way to coloured camera, resolution of picture captured enhanced, moving pictures were captured using monochrome T.V. Camera followed by coloured T.V. camera and now a days we have digital cameras as small as a size of button, embedded in our mobile phones, at a price, a student can afford from his pocket money. DOI: 10.17762/ijritcc2321-8169.16043

Remote sensing and GIS-based analysis of cave development in the Suoimuoi Catchment (Son La - NW Vietnam)

Integration of remotely sensed imagery with ground surveys is a promising method in cave development studies. In this research a methodology was set up in which a variety of remote sensing and GIS techniques support cave analysis in the tropical karst area of the Suoimuoi catchment, NW Vietnam. In order to extract the maximum information from different remotely sensed data, the hue invariant IHS transformation was applied to integrate Landsat multispectral channels with the high resolution Landsat 7 ETM panchromatic channel. The resulting fused image was used, after enhancement, to visually and digitally extract lineaments. Aerial photos evaluated the extracted lineaments. Based on lineament density indices a fracture zone favorable for cave development is defined. The distance between caves and faults was investigated as well as the correspondence between the cave occurrence and the fracture zone

Scale Stain: Multi-Resolution Feature Enhancement in Pathology Visualization

Digital whole-slide images of pathological tissue samples have recently become feasible for use within routine diagnostic practice. These gigapixel sized images enable pathologists to perform reviews using computer workstations instead of microscopes. Existing workstations visualize scanned images by providing a zoomable image space that reproduces the capabilities of the microscope. This paper presents a novel visualization approach that enables filtering of the scale-space according to color preference. The visualization method reveals diagnostically important patterns that are otherwise not visible. The paper demonstrates how this approach has been implemented into a fully functional prototype that lets the user navigate the visualization parameter space in real time. The prototype was evaluated for two common clinical tasks with eight pathologists in a within-subjects study. The data reveal that task efficiency increased by 15% using the prototype, with maintained accuracy. By analyzing behavioral strategies, it was possible to conclude that efficiency gain was caused by a reduction of the panning needed to perform systematic search of the images. The prototype system was well received by the pathologists who did not detect any risks that would hinder use in clinical routine

Automated microaneurysm detection algorithms applied to diabetic retinopathy retinal images

Diabetic retinopathy is the commonest cause of blindness in working age people. It is characterised and graded by the development of retinal microaneurysms, haemorrhages and exudates. The damage caused by diabetic retinopathy can be prevented if it is treated in its early stages. Therefore, automated early detection can limit the severity of the disease, improve the follow-up management of diabetic patients and assist ophthalmologists in investigating and treating the disease more efficiently. This review focuses on microaneurysm detection as the earliest clinically localised characteristic of diabetic retinopathy, a frequently observed complication in both Type 1 and Type 2 diabetes. Algorithms used for microaneurysm detection from retinal images are reviewed. A number of features used to extract microaneurysm are summarised. Furthermore, a comparative analysis of reported methods used to automatically detect microaneurysms is presented and discussed. The performance of methods and their complexity are also discussed

An Efficient Block-Based Algorithm for Hair Removal in Dermoscopic Images

Hair occlusion in dermoscopy images affects the diagnostic operation of the skin lesion. Segmentation and classification of skin lesions are two major steps of the diagnostic operation required by Dermatologists. We propose a new algorithm for hair removal in dermoscopy images that includes two main stages: hair detection and inpainting. In hair detection, a morphological bottom-hat operation is implemented on Y-channel image of YIQ color space followed by a binarization operation. In inpainting, the repaired Y-channel is partitioned into 256 nonoverlapped blocks and for each block, white pixels are replaced by locating the highest peak of using a histogram function and a morphological close operation. Our proposed algorithm reports a true positive rate (sensitivity) of 97.36%, a false positive rate (fall-out) of 4.25%, and a true negative rate (specificity) of 95.75%. The diagnostic accuracy achieved is recorded at a high level of 95.78%