Automatic segmentation of brain Magnetic Resonance Imaging (MRI) images is one of the vital steps for quantitative analysis of brain for further inspection. In this paper, NeuroNet has been adopted to segment the brain tissues (white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF)) which uses Residual Network (ResNet) in encoder and Fully Convolution Network (FCN) in the decoder. To achieve the best performance, various hyper-parameters have been tuned, while, network parameters (kernel and bias) were initialized using the NeuroNet pre-trained model. Different pre-processing pipelines have also been introduced to get a robust trained model. The model has been trained and tested on IBSR18 data-set. To validate the research outcome, performance was measured quantitatively using Dice Similarity Coefficient (DSC) and is reported on average as 0.84 for CSF, 0.94 for GM, and 0.94 for WM. The outcome of the research indicates that for the IBSR18 data-set, pre-processing and proper tuning of hyper-parameters for NeuroNet model have improvement in DSC for the brain tissue segmentation.

The segmentation is an essential part of many computer vision systems and medical applications. The goal is to divide an input image into a set of non-overlapping regions which union is the entire image. The Expectation-Maximization (EM) algorithm is an iterative technique designed for probabilistic models. Expectation-Maximization algorithm for segmenting brain MRI images (T1-w image) into the three main tissues: white matter (WM), grey matter (GM) and cerebrospinal fluid (CSF).

The idea of the project is to apply 2D U-net to Segment the RIO using the publicly available data from ISIC Challenge 2017 and classification of the segmented region for classification of lesion. Multiple OTS model ( Resnet, VGG, Densenet) were used for classification. Among the model VGG out performed all other model. Ensembling of the models was also applied and performed better than single classifier, Democracy always wins.

Lung diseases classification in 2D using chest CT cases and Analysis the multi-channel effect on classification. This work is been done during summer internship July-Aguest 2018, Duke University Medical Center.

Main idea of this project was making some new data using simple registration technique from existing CT data. This code be a possible alternative of arbitrary augmentation such as flip, rotation, zoom etc. A simple but powerful non-rigid registration technique BSpline is been used for this. There are number of parameters that can be used to make the variation of the registration to make new data which is completely different from Moving and Fixed CT used for registration.

Skin lesion segmentation is one of the first steps towards automatic Computer-Aided Diagnosis of skin cancer. Vast variety in the appearance of the skin lesion makes this task very challenging. The contribution of this paper is to apply a power foreground extraction technique called GrabCut for automatic skin lesion segmentation with minimal human interaction in HSV color space. Preprocessing was performed for removing the outer black border. Jaccard Index was measured to evaluate the performance of the segmentation method. On average, 0.71 Jaccard Index was achieved on 1000 images from ISIC challenge 2017 Training Dataset.

s the learning rate is one of the most importanthyper-parameters to tune for training convolutional neural net-works. In this paper, a powerful technique to select a range oflearning rates for a neural network that named cyclical learningrate was implemented with two different skewness degrees. Itis an approach to adjust where the value is cycled between alower bound and upper bound. CLR policies are computationallysimpler and can avoid the computational expense of fine tuningwith fixed learning rate. It is clearly shown that changing thelearning rate during the training phase provides by far betterresults than fixed values with similar or even smaller number of epochs.

Mammography is the most widely used gold standard method for the screening of the breast cancer and Mass detection is the prominent pre-processing step. State-of-art performances of the DCNN architectures in the field of classification made them an obvious choice for the image classifications. However, due to limited availability of medical data, application of DCNN architectures in medical images is challenging. In this project our contribution was mass detection in mammographic images using two different approaches. First approach was applying transfer learning concept which lessen the demand of data to use a pre-trained publicly available VGG16 model and second approach was training a Alexnet from the scratch to classify mass and non-mass mammographic images. Both the models were trained and tested with different hyperparameters and different data size. From the experiment results, it can be concluded that transfer learning approach for VGG16, training from fully connected layer 2 (fc2) has promising expected result for mass detection. In our research, maximum accuracy for mass detection was 93.60% for VGG16 with 13500 train images.

Design an inverse kinematic controller to move end-effector. This work is done as a coursework for the 2nd semester of MAIA in Introduction to Robotics course.The task was to design an Inverse Kinematic controller to move the end-effector from the position pA to the position pB The movement should be repeated 3 times by control: 1)the position-only without exploiting the redundancy 2)the position and the orientation without exploiting the redundancy (desired orientation equal to the initial one) 3)the position and the orientation avoiding an obstacle put in p0 (desired orientation equal to the initial one)

Left ventricular non-compaction (LVNC) is a rare cardiomyopathy (CMP) that should be considered as a possible diagnosis because of its potential complications which are heart failure, ventricular arrhythmias, and embolic events. For analysis cardiac functionality, extracting information from the Left ventricular (LV) is already a broad field of Medical Imaging. Different algorithms and strategies ranging that is semiautomated or automated has already been developed to get useful information from such a critical structure of heart. Trabeculae in the heart undergoes difference changes like solid from spongy. Due to failure of this process left ventricle non-compaction occurred. In this project, we will demonstrate the fractal dimension (FD) and manual segmentation of the Magnetic Resonance Imaging (MRI) of the heart that quantify amount of trabeculae inside the heart. The greater the value of fractal dimension inside the heart indicates the greater complex pattern of the trabeculae in the heart.

The availability of sophisticated source attribution techniques raises new concerns about privacy and anonymity of photographers, activists, and human right defenders who need to stay anonymous while spreading their images and videos. An image can be considered to be a combination of both significant (foreground) objects and some less significant (background) objects. Content aware image resizing (CAIR) algorithm uses the different edge detection methods to segregate the useful objects from the background. When applied to an image, CAIR can resize the image to a very different aspect ratio without destroying the aspect ratio of the useful objects in the image. In this project, we simply implement a content aware image resizing (CAIR) in MATLAB environment. The main idea to implement CAIR is to remove or insert the vertical or horizontal seams (paths of pixel) having the lowest energy. After implanted the Seam Carving Algorithm for Content aware image resizing (CAIR), analysis shows that the implemented seam carving for CAIR can generate more desirable resized images than cropping, resampling, and conventional seam carving techniques.

Few years back, face recognition is only limited to personal identification, now this technology is been grown in the field of security, healthcare, personalized services etc. Many methods are developed to perform for face recognition and they work quit well.The basic idea of this project it to compute a data set of features from the face images 𝐼𝑞which have compared with a set of database images 𝐼1………𝐼𝑞 to detect the best match. Every pixel can be considered as a variable that means the dimensional space is very high. PCA is been used to decreasing the dimensionality of the image for making this recognition problem easier.

This paper presents the design, development and implementation of a reconfigurable low-cost biopotential amplifier trainer module (RTR module) and quantitative analysis of the students’ compatibility with the trainer module. The trainer module can measure Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyogram (EMG) biopotential signals by reconfiguring the module using the basic circuit and filtering blocks. Given hand on experience, the module is designed and implemented in such reconfigurable manner that the students can avoid, disconnect and add any filtering blocks to understand the effect of these filters to the biopotential signals. The laboratory experience is an important component of the learning process. The RTR module is a low cost and compact educational tool. With this RTR module, the students should be able to recognize the biopotential signals and the acquisition methods in an intuitive and easy way, allowing them to improve their skills of designing biomedical instrumentation.

The name of our Application is ASHA Which mean Hope in Bengali. Here ASHA Stands for A Solution to Help Autism. The Idea of our App ASHA is to introduce a detection or test procedure for Autism.Autism is a spectrum of closely related disorders with a shared core of symptoms. Autism spectrum disorders appear in infancy and early childhood, causing delays in many basic areas of development, such as learning to talk, play, and interact with others.

In this blog, I will introduce a Library for 3D augmentations called volumentations-3D. .

Pre-processing for 3D Medical Imaging.

Trying cover the FAQs abour ERASMUS+ Scholarship funed By European Union.

Education with Travel & Cultural Experience.

A guide to writing a MOL/SOP.