Bioinformatics is a relatively new field of science that incorporates the principles of biology and computer science. It is mainly concerned with collecting, storing, and retrieving data from large databases. Ever since the successful completion of the Human Genome Project, there has been an exponential growth in the volumes of biological data that is being generated worldwide. The evolution of bioinformatics has made it possible to access these databases and apply the information for better research. One discipline that has been benefitted from the advent of bioinformatics is oral pathology. Oral pathology is a branch of dentistry which deals with the diseases of the head and neck region. Remarkable progress has been made in the diagnosis and treatment of diseases with the aid of bioinformatics. This article attempts to review the evolution and progress of dental informatics as a specialty and its applications in oral pathology.
Background:With the adoption of a completely electronic workflow by several journals and the advent of telepathology, digital imaging has become an integral part of every scientific research. However, manipulating digital images is very easy, and it can lead to misinterpretations.Aim:To analyse the impact of manipulating digital images on their diagnosis.Design:Digital images were obtained from Papanicolaou-stained smears of dysplastic and normal oral epithelium. They were manipulated using GNU Image Manipulation Program (GIMP) to alter their brightness and contrast and color levels. A Power Point presentation composed of slides of these manipulated images along with the unaltered originals arranged randomly was created. The presentation was shown to five observers individually who rated the images as normal, mild, moderate or severe dysplasia. Weighted κ statistics was used to measure and assess the levels of agreement between observers.Results:Levels of agreement between manipulated images and original images varied greatly among observers. Variation in diagnosis was in the form of overdiagnosis or under-diagnosis, usually by one grade.Conclusion:Global manipulations of digital images of cytological slides can significantly affect their interpretation. Such manipulations should therefore be kept to a minimum, and avoided wherever possible.
We propose a high efficient learning approach to estimating 6D (Degree of Freedom) pose of the textured or texture-less objects for grasping purposes in a cluttered environment where the objects might be partially occluded. The method comprises three main steps. Given a single RGB-D image, we first deploy appropriate features and the random forest to deduce the object class probability and cast votes for the 6D pose in Hough space by joint regression and classification framework, adopting reservoir sampling and summarizing the pose distribution by clustering. Next, we integrate the auto-context into cascaded Hough forests to improve the efficiency of learning. Extensive experiments on various public datasets and robotic grasps indicate that our method presents some improvements over the state-of-art and reveals the capability for estimating poses in practical applications efficiently.
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