Abstract: With the advancement in the field of Artificial Intelligence, there have been considerable efforts to develop technologies for pattern recognition related to medical diagnosis. Artificial Neural Networks (ANNs), a significant piece of Artificial Intelligence forms the base for most of the marvels in the former field. However, ANNs face the problem of premature convergence at a local minimum and inability to set hyper-parameters (like the number of neurons, learning rate, etc.) while using Back Propagation Algorithm (BPA). In this paper, we have used the Genetic Algorithm (GA) for the evolution of the ANN, which overcomes the limitations of the BPA. Since GA alone cannot fit for a high-dimensional, complex and multi-modal optimization landscape of the ANN, BPA is used as a local search algorithm to aid the evolution. The contributions of GA and BPA in the resultant approach are adjudged to determine the magnitude of local search necessary for optimization, striking a clear balance between exploration and exploitation in the evolution. The algorithm was applied to deal with the problem of Breast Cancer diagnosis. Results showed that under optimal settings, hybrid algorithm performs better than BPA or GA alone.
Abstract: Magnetic Resonance Imaging (MRI) plays a significant role in the current characterization and diagnosis of multiple sclerosis (MS) in radiological imaging. However, early detection of MS lesions from MRI still remains a challenging problem. In the present work, an information theoretic approach to cluster the voxels in MS lesions for automatic segmentation of lesions of various sizes in multi-contrast (T1, T2, PD-weighted) MR images, is applied. For accurate detection of MS lesions of various sizes, the skull-stripped brain data are rescaled and histogram manipulated prior to mapping the multi-contrast data to pseudo-color images. For automated segmentation of multiple sclerosis (MS) lesions in multi-contrast MRI, the improved jump method (IJM) clustering method has been enhanced via edge suppression for improved segmentation of white matter (WM), gray matter (GM), cerebrospinal fluid (CSF) and MS lesions if present. From this preliminary clustering, a pseudo-color to grayscale conversion is designed to equalize the intensities of the normal brain tissues, leaving the MS lesions as outliers. Binary discrete and 8-bit fuzzy labels are then assigned to segment the MS lesions throughout the full brain. For validation of the proposed method, three brains, with mild, moderate and severe hyperintense MS lesions labeled as ground truth, were selected. The MS lesions of mild, moderate and severe categories were detected with a sensitivity of 80%, and 96%, and 94%, and with the corresponding Dice similarity coefficient (DSC) of 0.5175, 0.8739, and 0.8266 respectively. The MS lesions can also be clearly visualized in a transparent pseudo-color computer rendered 3D brain.
Abstract: Currently, anatomically consistent segmentation of vascular trees acquired with magnetic resonance imaging requires the use of multiple image processing steps, which, in turn, depend on manual intervention. In effect, segmentation of vascular trees from medical images is time consuming and error prone due to the tortuous geometry and weak signal in small blood vessels. To overcome errors and accelerate the image processing time, we introduce an automatic image processing pipeline for constructing subject specific computational meshes for entire cerebral vasculature, including segmentation of ancillary structures; the grey and white matter, cerebrospinal fluid space, skull, and scalp. To demonstrate the validity of the new pipeline, we segmented the entire intracranial compartment with special attention of the angioarchitecture from magnetic resonance imaging acquired for two healthy volunteers. The raw images were processed through our pipeline for automatic segmentation and mesh generation. Due to partial volume effect and finite resolution, the computational meshes intersect with each other at respective interfaces. To eliminate anatomically inconsistent overlap, we utilized morphological operations to separate the structures with a physiologically sound gap spaces. The resulting meshes exhibit anatomically correct spatial extent and relative positions without intersections. For validation, we computed critical biometrics of the angioarchitecture, the cortical surfaces, ventricular system, and cerebrospinal fluid (CSF) spaces and compared against literature values. Volumina and surface areas of the computational mesh were found to be in physiological ranges. In conclusion, we present an automatic image processing pipeline to automate the segmentation of the main intracranial compartments including a subject-specific vascular trees. These computational meshes can be used in 3D immersive visualization for diagnosis, surgery planning with haptics control in virtual reality. Subject-specific computational meshes are also a prerequisite for computer simulations of cerebral hemodynamics and the effects of traumatic brain injury.
Abstract: This study presents microwave absorption of raw materials used in barium borosilicate, Fe-doped alumina phosphate and zinc borate glass. Microwave absorption was investigated for the raw materials SiO2, Na2CO3, BaCO3, BPO4, Al(PO3)3, Mg(PO3)2, Al(OH)3, TiO2. The study shows that SiO2 could be heated directly above 1000 °C within 30 min at 1.5 kW microwave output (MW) power and 0.8 kW MW power is necessary to initiate heating (from 260 °C). Microwave heating of material with low dielectric loss has been investigated by increasing MW power. Microwave absorption of above glass systems has also been investigated. Dielectric properties such as loss tangent of glass as a function of temperature are presented. Glass melting under direct microwave heating was demonstrated for the studied glass systems. Temperature-Microwave power-Time (T-P-t) profiles for the three glasses indicate maximum MW output power ~1 kW, 0.65 kW and ~1 kW for barium borosilicate, zinc borate glass and alumino-phosphate glass for 60 g glass melting.
Abstract: This paper addresses a new hybrid feature extractor algorithm, which in essence integrates a Fast-Hessian detector into the SIFT (Scale Invariant Feature Transform) algorithm. Feature extractors mainly consist of two essential parts: feature detector and descriptor extractor. This study proposes to integrate (Speeded-Up Robust Features) SURF’s hessian detector into the SIFT algorithm so as to boost the total number of true matched pairs. This is a critical requirement in image processing and widely used in various corresponding fields from image stitching to object recognition. The proposed hybrid algorithm has been tested under different experimental conditions and results are quite encouraging in terms of obtaining higher matched pairs and precision score.
Abstract: In the current research, the role of nano-sized alumina on deformation and fracture mechanism of Poly Methyl Methacrylate (PMMA) was investigated. For this purpose, PMMA matrix nanocomposite reinforced with different wt% of alumina (i.e., 5, 10 and 15) were fabricated using the compression molding technique. Tensile properties of produced nanocomposites were studied using Zwick Z250 apparatus at cross head speed of about 5 mm/min. In order to specify the role of alumina nanoparticles on deformation and fracture mechanism of PMMA, microscopic evaluation was performed using scanning electron microscope (SEM). The achieved results prove that tensile properties of PMMA depend on alumina wt%. For example, addition of 15 wt% alumina to PMMA causes an increase of about 25% modulus of elasticity. Micrographs taken from the fracture surface of PMMA and its nanocomposites show deformation and fracture mechanism of PMMA changes as alumina is added to it.