Computer-Aided detection of tuberculosis from X-ray images using CNN and PatternNet classifier.

Background: Tuberculosis (TB) is a highly infectious disease that mainly affects the human lungs. The gold standard for TB diagnosis is Xpert Mycobacterium tuberculosis/ resistance to rifampicin (MTB/RIF) testing. X-ray, a relatively inexpensive and widely used imaging modality, can be employed as an alternative for early diagnosis of the disease.

Multi-class diagnosis of Alzheimer's disease using cascaded three dimensional-convolutional neural network.

Dementia is a social problem in the aging society of advanced countries. Presently, 46.8 million people affected with dementia worldwide, and it may increase to 130 million by 2050.

Automated segmentation of optic disc using statistical region merging and morphological operations.

Accurate Optic Disc (OD) segmentation is vital in designing systems that aid the diagnosis and evaluation of early phases of retinal diseases. However, in many images, the OD boundary is ambiguous, which makes the automated OD segmentation process very challenging. A method to segment OD based on statistical region merging and morphological operations is proposed in this paper.

A sparsity-based iterative algorithm for reconstruction of micro-CT images from highly undersampled projection datasets obtained with a synchrotron X-ray source.

Synchrotron X-ray Micro Computed Tomography (Micro-CT) is an imaging technique which is increasingly used for non-invasive in vivo preclinical imaging. However, it often requires a large number of projections from many different angles to reconstruct high-quality images leading to significantly high radiation doses and long scan times. To utilize this imaging technique further for in vivo imaging, we need to design reconstruction algorithms that reduce the radiation dose and scan time without reduction of reconstructed image quality.