Quantum cellular automaton (QCA) is an efficient and emerging nanotechnology to create quantum computing devices. It is polarization based digital logic architecture. QCA cell is the basic unit to build logic gates and devices in quantum domain. This paper proposes an effective design of logic gates and arithmetic circuit using QCA. Here the gates and circuits are designed using minimum number of QCA cells and with no crossovers. So these designs can be used to construct complex circuits. The simulations of the present work have been carried out by means of QCA designer tools. The simulation results help to implement the digital circuits in nanoscale range.

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MRI imaging technique is used to detect spine tumours. After getting the spine image through MRI scans calculation of area, size, and position of the spine tumour are important to give treatment for the patient. The earlier the tumour portion of the spine is detected using manual labeling. This is a challenging task for the radiologist, and also it is a time-consuming process. Manual labeling of the tumour is a tiring, tedious process for the radiologist. Accurate detection of tumour is important for the doctor because by knowing the position and the stage of the tumour, the doctor can decide the type of treatment for the patient. Next, important consideration in the detection of a tumour is earlier diagnosis of a tumour; this will improve the lifetime of the patient. Hence, a method which helps to segment the tumour region automatically is proposed. Most of the research work uses clustering techniques for segmentation. The research work used k-means clustering and active contour segmentation to find the tumour portion.

The images of disease-affected and normal eyes collected from high-resolution fundus (HRF) image database are analyzed, and the influence of ocular diseases on iris using a reliable fuzzy recognition scheme is proposed. Nearly 45 samples of iris images are acquired using Canon CR-1 fundus camera with a field of view of 45° when subjected to routine ophthalmology visits, and the samples of eye images include healthy eyes, eyes affected by glaucoma, cataract, and diabetic retinopathy. These images are then subjected to various image processing techniques like pre-processing for de-noising using blind de-convolution, wavelet-based feature extraction, principal component analysis (PCA) for dimension reductionality, followed by fuzzy c-means clustering inference scheme to categorize the normal and diseased eyes. It is inferred that the proposed method takes only two minutes with an accuracy, specificity, and sensitivity varying in the range of 94% to 98%, respectively.

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