Development of automatic glioma brain tumor detection system using deep convolutional neural networks

We have developed six convolutional neural network (CNN) models for finding optimal brain tumor detection system on high-grade glioma and low-grade glioma lesions from voluminous magnetic resonance imaging human brain scans. Glioma is the most common form of brain tumor. The models are constructed based on the different combinations and settings of hyperparameters with conventional CNN architecture. The six models are two layers with five epochs, five layers with dropout, five layers with stopping criteria (FLSC), FLSC and dropout (FLSCD), FLSC and batch normalization (FLSCBN), and FLSCBN and dropout. The models were trained and tested with BraTS2013 and whole brain atlas data sets. Among them, FLSCBN model yielded the best classification results for brain tumor detection. Experimental results revealed that our deep learning approach was better than the conventional state-of-art methods.     

Lower limb gigantism,lymphedema, and painful varicosities following a thigh vascular access graft

Prosthetic arteriovenous grafts (AVGs) are associated with greater morbidity than autogenous arteriovenous fistulas (AVFs), but their use is indicated when AVF formation is not possible. This report adds to the literature a case of lower limb gigantism, painful varicosities, and lymphedema following long‐term use of AVG in the upper thigh. The patient's past medical history included renal transplantation on the same side well before the AVG was inserted and right leg deep vein thrombosis. Suspicion of AVG thrombosis was excluded by Doppler ultrasound, which demonstrated an access flow of 1700 mL/min. A computed tomography (CT) scan of the abdomen and pelvis did not identify the cause of her symptoms. Whereas functional incompetence of the iliac vein valve might be responsible for the varicosities, the extent of hypertrophy in this case raises the suspicion of lymphatic blockage possibly secondary to groin dissection undertaken at the time of graft insertion, in addition to the previous dissection at the time of transplantation. This case highlights the need for minimal groin dissection during AVG insertion, particularly in patients with a history of previous abdominopelvic surgery.     

PET image reconstruction using ANN

The aim of this study is to improve the positron emission tomography (PET) image quality for medical diagnosis. The statistical reconstructions on the maximum a posteriori (MAP) algorithm often results in a blurring effect, which fails to determine the toughness class in the reconstructed image. The development of new reconstruction algorithms for PET is an active field of research. In this article, artificial neural network (ANN) is proposed for replicating the output image, which is generated from the acquired projection data with the corresponding angles using the PET images. This article proposes the advantage of arranging the neural network to stock up the information of the continuous capacity. This reduces the storage space and recuperates as much sequence of the continuous quantity as possible. The performance of image quality parameters using ANN is better when compared with MAP, FBP‐NN (filtered back projection with nearest neighbor interpolation). Thus ANN provides 63% better peak signal to noise ratio (PSNR) when compared with FBP‐NN and 47% better when compared to MAP. Thus, ANN is better than FBP and MAP algorithm, by providing better PSNR. © 2014 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 24, 249–255, 2014     

Investigation of DS Furnace Heat Exchange Block Thickness for the Improvement mc-Si Ingot Quality

Silicon - Numerical investigation was performed for analyzing the distribution of von Mises stress in the growing multi-crystalline silicon ingot during the directional solidification process. In...     

Lossless color medical image compression using adaptive block‐based encoding for human computed tomographic images

The advancement in medical imaging systems such as computed tomography (CT), magnetic resonance imaging (MRI), positron emitted tomography (PET), and computed radiography (CR) produces huge amount of volumetric images about various anatomical structure of human body. There exists a need for lossless compression of these images for storage and communication purposes. The major issue in medical image is the sequence of operations to be performed for compression and decompression should not degrade the original quality of the image, it should be compressed loss lessly. In this article, we proposed a lossless method of volumetric medical image compression and decompression using adaptive block‐based encoding technique. The algorithm is tested for different sets of CT color images using Matlab. The Digital Imaging and Communications in Medicine (DICOM) images are compressed using the proposed algorithm and stored as DICOM formatted images. The inverse process of adaptive block‐based algorithm is used to reconstruct the original image information loss lessly from the compressed DICOM files. We present the simulation results for large set of human color CT images to produce a comparative analysis of the proposed methodology with block‐based compression, and JPEG2000 lossless image compression technique. This article finally proves the proposed methodology gives better compression ratio than block‐based coding and computationally better than JPEG 2000 coding. © 2013 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 23, 227–234, 2013     

A new red colour emitting phosphor – ZnS:Mn co-doped with Ba for electroluminescent (EL) displays

A new Mn activated Ba co-doped ZnS phosphor shows an interesting shift in the spectral emission from amber-yellow (570 nm) to red (620 nm) colour. To realize the phosphor material physics, attempts were made to fine tune the band gap of ZnS by co-doping Ba within the concentration range 2.50–10 mol%. Ba co-doping in ZnS host was achieved by sintering BaSO₄ with ZnS at 900 °C by carbothermal reduction method. Measurement of the CIE (Commission International deEchairge) chromaticity colour coordinates (x, y) of Zn_0.99S:Mn_0.01 and Ba_0.10Zn_0.89S:Mn_0.01 phosphors shown to be (0.53, 0.47) and (0.65, 0.32), respectively.