Suspicious Lesion Segmentation on Brain, Mammograms and Breast MR Images Using New Optimized Spatial Feature Based Super-Pixel Fuzzy C-Means Clustering.

Suspicious lesion or organ segmentation is a challenging task to be solved in most of the medical image analyses, medical diagnoses and computer diagnosis systems. Nevertheless, various image segmentation methods were proposed in the previous studies with varying success levels. But, the image segmentation problems such as lack of versatility, low robustness, high complexity and low accuracy in up-to-date image segmentation practices still remain unsolved. Fuzzy c-means clustering (FCM) methods are very well suited for segmenting the regions. The noise-free images are effectively segmented using the traditional FCM method. However, the segmentation result generated is highly sensitive to noise due to the negligence of spatial information. To solve this issue, super-pixel-based FCM (SPOFCM) is implemented in this paper, in which the influence of spatially neighbouring and similar super-pixels is incorporated. Also, a crow search algorithm is adopted for optimizing the influential degree; thereby, the segmentation performance is improved. In clinical applications, the SPOFCM feasibility is verified using the multi-spectral MRIs, mammograms and actual single spectrum on performing tumour segmentation tests for SPOFCM. Ultimately, the competitive, renowned segmentation techniques such as k-means, entropy thresholding (ET), FCM, FCM with spatial constraints (FCM_S) and kernel FCM (KFCM) are used to compare the results of proposed SPOFCM. Experimental results on multi-spectral MRIs and actual single-spectrum mammograms indicate that the proposed algorithm can provide a better performance for suspicious lesion or organ segmentation in computer-assisted clinical applications.

Theranostic applications of nanoparticles in neurodegenerative disorders.

The preeminent treatments for neurodegenerative disease are often unavailable due to the poor accessibility of therapeutic drugs. Moreover, the blood-brain barrier (BBB) effectively blocks the transfer of cells, particles and large molecules, ie, drugs, across the brain. The most important challenge in the treatment of neurodegenerative diseases is the development of targeted drug delivery system. Theranostic strategies are known to combine therapeutic and diagnostic capabilities together. The aim of this review was to record the response to treatment and thereby improve drug safety. Nanotechnology offers a platform for designing and developing theranostic agents that can be used as an efficient nano-carrier system. This is achieved by the manipulation of some of the properties of nanoparticles (NPs), thereby enabling the attachment of suitable drugs onto their surface. The results provide revolutionary treatments by stimulation and thus interaction with targeted sites to promote physiological response with minimum side effects. This review is a brief discussion of the administration of drugs across the brain and the advantages of using NPs as an effective theranostic platform in the treatment of Alzheimer's, Parkinson's, epilepsy and Huntington's disease.

Feature Level Two -Dimensional Arrays Based Fusion in the Personal Authentication system using Physiological Biometric traits

ABSTRACT The fingerprint, knuckle print and the retina are used to authenticate a person accurately because of the permanence in the features. These three biometric traits are fused for better security. The fingerprint and knuckle print images are pre-processed by morphological techniques and the features are extracted from the normalized image using gabor filter. The retinal image is converted to gray image and pre-processing is done using top hat and bottom hat filtering. Blood vessels are segmented and the features are extracted by locating the optic disk as the centre point. The extracted features from the fingerprint, knuckle print and the retina are fused together as one template and stored in the data base for authentication purpose, thus reducing the space and time complexity.