A Hybrid Enhanced Independent Component Analysis Approach for Segmentation of Brain Magnetic Resonance Image

Medical imaging and analysis plays a crucial role in diagnosis and treatment planning. The anatomical complexity of human brain makes the process of imaging and analyzing very difficult. In spite of huge advancements in medical imaging procedures, accurate segmentation and classification of brain abnormalities remains a challenging and daunting task. This challenge is more visible in the case of brain tumors because of different possible shapes of tumors, locations and image intensities of different types of tumors. In this paper we have presented a method for automated segmentation of brain tumors from magnetic resonance images. An enhanced and modified Gaussian mixture mode model and the independent component analysis segmentation approach has been employed for segmenting brain tumors in magnetic resonance images. The results of segmentation are validated with the help of segmentation evaluation parameters

Unsupervised cell segmentation and labelling in neural tissue images

Neurodegenerative diseases are a group of largely incurable disorders characterised by the progressive loss of neurons and for which often the molecular mechanisms are poorly understood. To bridge this gap, researchers employ a range of techniques. A very prominent and useful technique adopted across many different fields is imaging and the analysis of histopathological and fluorescent label tissue samples. Although image acquisition has been efficiently automated recently, automated analysis still presents a bottleneck. Although various methods have been developed to automate this task, they tend to make use of single-purpose machine learning models that require extensive training, imposing a significant workload on the experts and introducing variability in the analysis. Moreover, these methods are impractical to audit and adapt, as their internal parameters are difficult to interpret and change. Here, we present a novel unsupervised automated schema for object segmentation of images, exemplified on a dataset of tissue images. Our schema does not require training data, can be fully audited and is based on a series of understandable biological decisions. In order to evaluate and validate our schema, we compared it with a state-of-the-art automated segmentation method for post-mortem tissues of ALS patients

Appearance-based Keypoint Clustering

We present an algorithm for clustering sets of detected interest points into groups that correspond to visually distinct structure. Through the use of a suitable colour and texture representation, our clustering method is able to identify keypoints that belong to separate objects or background regions. These clusters are then used to constrain the matching of keypoints over pairs of images, resulting in greatly improved matching under difficult conditions. We present a thorough evaluation of each component of the algorithm,and show its usefulness on difficult matching problems