“…The improved possibilistic c‐means (IPCM) clustering, as a derivative algorithm of FCM and PCM, offers both the fuzzy membership values and typical values simultaneously (Zhang & Leung, 2004). Gustafson–Kessel (GK) clustering recognizes the clustering structures of different data shapes by replacing Euclidean distance with Mahalanobis distance in the objective function (Chaomurilige, Yu, & Yang, 2017). Fuzzy clustering algorithms are applied to classify tea varieties, which can achieve rapid, effective, and nondestructive identification effect.…”
“…The improved possibilistic c‐means (IPCM) clustering, as a derivative algorithm of FCM and PCM, offers both the fuzzy membership values and typical values simultaneously (Zhang & Leung, 2004). Gustafson–Kessel (GK) clustering recognizes the clustering structures of different data shapes by replacing Euclidean distance with Mahalanobis distance in the objective function (Chaomurilige, Yu, & Yang, 2017). Fuzzy clustering algorithms are applied to classify tea varieties, which can achieve rapid, effective, and nondestructive identification effect.…”
“…For partitioning the dynamic data of state variable an interval adaptive similarity mechanism is adopted to define interval operation regions represented by interval membership functions, with different formats and orientations, adapted to the topological structure associated to the variability of dynamic data, in order to minimize the filtering errors (Babuska 1998 ; Höppner et al. 1999 ; Chaomurilige and Yang 2017 ). Fig.…”
“…The alternative approach is to use 3D images to segment a region of interest first then perform the object classification on a much smaller 3D voxel structure which will decreases the hardware requirements needed and the computing time. Multiple studies showed that the results accuracy increases if the region of interest (ROI) is extracted first with the subsequent object classification (El-Dahshan et al, 2014;Shankar et al, 2016;Yu and Yang, 2017).…”
Section: D Analysis Vs 3d Reconstructionsmentioning
confidence: 99%
“…For brain tumor segmentation El-dashan et al were able to produce the classification accuracy of 99% using a Feedback Pulse-Coupled Neural Network (FPCNN), a type of CNN architecture, for ROI segmentation and a feed forward ANN for image classification (El-Dahshan et al, 2014). Shankar et al achieved an accuracy of 95.67% using the Gustafson-Kessel fuzzy clustering algorithm for classification (Shankar et al, 2016;Yu and Yang, 2017). Alakwaa et al's implementation used a CNN inspired architecture called U-Net to extract the ROI and the subsequent 3D CNN to classify the abnormal vs. normal tissue in the lung images (Ronneberger et al, 2015).…”
Section: Model and Diagnostic Performancementioning
In recent years, a number of new products introduced to the global market combine intelligent robotics, artificial intelligence and smart interfaces to provide powerful tools to support professional decision making. However, while brain disease diagnosis from the brain scan images is supported by imaging robotics, the data analysis to form a medical diagnosis is performed solely by highly trained medical professionals. Recent advances in medical imaging techniques, artificial intelligence, machine learning and computer vision present new opportunities to build intelligent decision support tools to aid the diagnostic process, increase the disease detection accuracy, reduce error, automate the monitoring of patient's recovery, and discover new knowledge about the disease cause and its treatment. This article introduces the topic of medical diagnosis of brain diseases from the MRI based images. We describe existing, multi-modal imaging techniques of the brain's soft tissue and describe in detail how are the resulting images are analyzed by a radiologist to form a diagnosis. Several comparisons between the best results of classifying natural scenes and medical image analysis illustrate the challenges of applying existing image processing techniques to the medical image analysis domain. The survey of medical image processing methods also identified several knowledge gaps, the need for automation of image processing analysis, and the identification of the brain structures in the medical images that differentiate healthy tissue from a pathology. This survey is grounded in the cases of brain tumor analysis and the traumatic brain injury diagnoses, as these two case studies illustrate the vastly different approaches needed to define, extract, and synthesize meaningful information from multiple MRI image sets for a diagnosis. Finally, the article summarizes artificial intelligence frameworks that are built as multi-stage, hybrid, hierarchical information processing work-flows and the benefits of applying these models for medical diagnosis to build intelligent physician's aids with knowledge transparency, expert knowledge embedding, and increased analytical quality.
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