Mapping Activity Area Localization in Functional MRI Imaging with Deep Learning based Automatic Segmented Brain Tumor for Presurgical Tumor Resection Planning

Functional Magnetic Resonance Imaging (fMRI) determines small blood flow variations that arise due to brain activity. fMRI major study is about functional anatomy which determines the area of the brain controlling vital functions such as hand and foot motor movements for both left and right, speech mantra, and speech word activities. For this instinctive localization of activity areas for specific tasks is very important. This paper appropriately describes the fMRI paradigm timeline with a modified fMRI paradigm timeline due to the hemodynamic response function (HRF).   Efficient activity area localization of thirty-three patients for fMRI data acquired from the hospital is achieved with dynamic thresholding. Dynamic thresholding is also effective in removing excess highlighted areas which helps in the reduction in expert efforts and time required to generate the patient report.  The localize activity area is further mapped with deep learning-based automatic segmented brain tumor regions to find overlapping regions. The exact location of the overlapping region is recovered which helps with preoperative counseling and tumor resection planning. All the results are verified and validated by two expert radiologists from the Hospital

Efficient segmentation and classification of the tumor using improved encoder-decoder architecture in brain MRI images

Primary diagnosis of brain tumors is crucial to improve treatment outcomes for patient survival. T1-weighted contrast-enhanced images of Magnetic Resonance Imaging (MRI) provide the most anatomically relevant images. But even with many advancements, day by day in the medical field, assessing tumor shape, size, segmentation, and classification is very difficult as manual segmentation of MRI images with high precision and accuracy is indeed a time-consuming and very challenging task. So newer digital methods like deep learning algorithms are used for tumor diagnosis which may lead to far better results. Deep learning algorithms have significantly upgraded the research in the artificial intelligence field and help in better understanding medical images and their further analysis. The work carried out in this paper presents a fully automatic brain tumor segmentation and classification model with encoder-decoder architecture that is an improvisation of traditional UNet architecture achieved by embedding three variants of ResNet like ResNet 50, ResNet 101, and ResNext 50 with proper hyperparameter tuning. Various data augmentation techniques were used to improve the model performance. The overall performance of the model was tested on a publicly available MRI image dataset containing three common types of tumors. The proposed model performed better in comparison to several other deep learning architectures regarding quality parameters including Dice Similarity Coefficient (DSC) and Mean Intersection over Union (Mean IoU) thereby enhancing the tumor analysis

Face Recognition Using Discrete Cosine Transform for Global and Local Features

Face Recognition using Discrete Cosine Transform (DCT) for Local and Global Features involves recognizing the corresponding face image from the database. The face image obtained from the user is cropped such that only the frontal face image is extracted, eliminating the background. The image is restricted to a size of 128 x 128 pixels. All images in the database are gray level images. DCT is applied to the entire image. This gives DCT coefficients, which are global features. Local features such as eyes, nose and mouth are also extracted and DCT is applied to these features. Depending upon the recognition rate obtained for each feature, they are given weightage and then combined. Both local and global features are used for comparison. By comparing the ranks for global and local features, the false acceptance rate for DCT can be minimized.Comment: face recognition; biometrics; person identification; authentication; discrete cosine transform; DCT; global local features; Proceedings of the 2011 International Conference on Recent Advancements in Electrical, Electronics and Control Engineering (IConRAEeCE) IEEE Xplore: CFP1153R-ART; ISBN: 978-1-4577-2149-

Identification of genomic regions linked to seed dormancy related traits using bulk segregant analysis in rice (Oryza sativa L.)

A total of 119 F6:7 RILs of a cross between BPT 2231 (non- seed dormant parent) and MTU 1001 (seed dormant parent) were analyzed to identify the markers associated with seed dormancy. Parental polymorphism survey with 188 SSR markers revealed 10 polymorphic markers between the parents. The bulk segregant analysis results with 10 polymorphic markers revealed that four markers showed polymorphism between the bulks. The association of putative markers viz., RM346, RM22565, RM7051 and RM10793 identified based on DNA pooling from selected segregants was analyzed by Single Marker Anaysis (SMA). The results of SMA revealed that RM22565 on chromosome 8 showed significant association with germination per cent at five days after harvesting indicating that the chromosomal region linked to the marker RM 22565 on chromosome 8 may be associated with seed dormancy. Out of the four polymorphic markers used in the present study, RM346 was notified as a seed dormancy linked marker from previous studies. The other three markers viz., RM22565, RM7051 and RM10793 identified as seed dormancy linked markers in the present study, needs further validation on alternative set of population or a set of germplasm lines for their further utilization in the marker assisted breeding programme. Based on germination percentage, physiological parameters and genotyping studies, the RILs viz., SD 3, SD 12, SD 15 and SD 68 were identified as donors for the future breeding programme for the development of seed dormant varieties