Development of image-processing software for automatic segmentation of brain tumors in MR images

Most of the commercially available software for brain tumor segmentation have limited functionality and frequently lack the careful validation that is required for clinical studies. We have developed an image-analysis software package called ‘Prometheus,’ which performs neural system–based segmentation operations on MR images using pre-trained information. The software also has the capability to improve its segmentation performance by using the training module of the neural system. The aim of this article is to present the design and modules of this software. The segmentation module of Prometheus can be used primarily for image analysis in MR images. Prometheus was validated against manual segmentation by a radiologist and its mean sensitivity and specificity was found to be 85.71±4.89% and 93.2±2.87%, respectively. Similarly, the mean segmentation accuracy and mean correspondence ratio was found to be 92.35±3.37% and 0.78±0.046, respectively

Detection of epileptic seizures on EEG signals using Decision Tree, KNN, SVM and ensemble classifiers

Epilepsy is a neurological condition resulting to brain cell stimulation. According to the findings of the research, an electroencephalogram (EEG) can identify epileptic episodes in patients who have epilepsy. Performance evaluations based on EEG detection of epilepsy require feature extraction methods. As a result of our research, we were able to identify a number of different feature extraction approaches. These methodologies were dependent upon nonlinear, wavelet-based entropy characteristics, time - frequency domain characteristics, and a few statistical traits. An additional in-depth examination that made use of cutting-edge machine learning classifiers as well as numerous factors was carried out. When evaluating kernels for support vector machines, the multiclass kernel as well as the box constraint levels are both useful tools. In a similar manner, we computed the various distance measures, neighbour weights, and neighbour relationships for the K-nearest neighbours (KNN). In a similar manner, we altered the decision trees of the paramours depending on maximal splits as well as split criteria, and we evaluated the ensemble classifiers based on a variety of ensemble approaches and learning rates.&nbsp

A novel computational approach of image analysis to quantify behavioural response to heat shock in Chironomus Ramosus larvae (Diptera: Chironomidae)

All living cells respond to temperature stress through coordinated cellular, biochemical and molecular events known as “heat shock response” and its genetic basis has been found to be evolutionarily conserved. Despite marked advances in stress research, this ubiquitous heat shock response has never been analysed quantitatively at the whole organismal level using behavioural correlates. We have investigated behavioural response to heat shock in a tropical midge Chironomus ramosus Chaudhuri, Das and Sublette. The filter-feeding aquatic Chironomus larvae exhibit characteristic undulatory movement. This innate pattern of movement was taken as a behavioural parameter in the present study. We have developed a novel computer-aided image analysis tool “Chiro” for the quantification of behavioural responses to heat shock. Behavioural responses were quantified by recording the number of undulations performed by each larva per unit time at a given ambient temperature. Quantitative analysis of undulation frequency was carried out and this innate behavioural pattern was found to be modulated as a function of ambient temperature. Midge larvae are known to be bioindicators of aquatic environments. Therefore, the “Chiro” technique can be tested using other potential biomonitoring organisms obtained from natural aquatic habitats using undulatory motion as a behavioural parameter