Our objective is to study the prediction of the survival time of patients with metastatic melanoma in terms of 1-year survival as a binary classification. Dataset used in this study contains CTs of 71 patients with metastatic melanoma who are studied at Universite Clermont Auvergne Hospital. The number of lesions per patient varies from 1 to 11. To reach the objective, the survival time is anticipated using the accessible CT data as input of a 3D CNN. In this category, survival time is anticipated using full CT volumes and also from extracted 3D CT patches containing lesion regions. Here, the patches are extracted given the ground truth masks of the lesions. Moreover, segmentation of lesions coming from different organs is performed using two different 3D CNNs to examine the prediction of survival time based on newly extracted 3D CT patches. These new patches are extracted using our anticipated segmentation predicted masks of lesions. As the final test, it is also inspected whether aggregated deep segmentation feature map can help to predict survival time being an extra input channel to the CT data for 3D CNN or not. Our study shows that using aggregated deep segmentation feature map as an extra input channel to CT data comes about in superior performance in survival time prediction compared to using as it were only 3D CT patches as input. In expansion, the prediction of survival time based on newly extracted 3D CT patches coming from our segmentation predicted masks is similar to the survival prediction using the 3D CT patches coming from ground truth masks. Further investigation of this study can be the addition of radiomic features with aggregated deep segmentation feature map as additional input to CT data besides experimenting on bigger dataset. Including clinical data such as age, sex, etc. can as well play a vital role.

The main goal of this project is to develop tissue (WM, GM, and CSF) segmentation methods in brain MRI images. Dataset used is IBSR18 which contains 18 skull-stripped and bias field corrected T1-w images with different spatial resolutions (pixel spacing) and there is a heterogeneity in image intensities which hinders segmentation. System architecture- Data → Pre-processing → Segmentation. Steps- 1) Pre-processing (a) Normalization and Skull stripping of MNI template, b) Image Registration (We have used Elastix as a software to apply 3D registration of moving image on fixed), c) Histogram Stretching (The registered volumes’ histograms have been stretched which broadens the histogram of the image intensity levels, d) Histogram Matching (Histogram stretched output of IBSR 07 has been taken as reference to match all other volumes’ stretched histogram), and 2) Segmentation- 3D patch-wise segmentation using 3D U-Net.

The main objective of this project was to design a Computer Aided Diagnosis system to develop a deep learning algorithm for diagnosis in histological patches to classify benign vs malignant patches. System architecture- Image → Normalization → Per instance standardization → Aggressive Data Augmentation → Classification. Steps- 1) Data normalization, 2) Per instance standardization (Standardization by mean and std of the training dataset), 3) Aggressive Data Augmentation (Horizontal flip, Vertical flip), and 4) Classification; Best performing classification model- ResnNet50 (ImageNet-pretrained).

The main goal of this project was to design a Computer Aided Diagnosis system to classify dermoscopic images whether any case belongs to Nevus or lesion. System architecture- Image → Normalization → Per instance standardization → Aggressive Data Augmentation → Classification. Steps- 1) Data normalization, 2) Per instance standardization (Standardization by mean and std of the training dataset), 3) Aggressive Data Augmentation (Rotation, Width and Height shift, Zooming, Flipping, Brightness), 4) Classification; Best performing classification model- Ensemble of Inception ResNetv2, Inception v3, EfficientNet B3 (ImageNet-pretrained).

This project aims to register 3D CT lung images which was tested on first 4 cases available on 4DCT DIR-Lab Challenge. Where, data is available with 300 landmark annotations. Evaluation is made using TRE 3D Euclidean distance between transformed landmarks.

The goal of this project was to demonstrate pedicle screw placement for spine fusion. Steps- 1) Trajectory planning (Type of movement is Point to point), 2) Software simulation of robotic spine surgery (Stäubli Robotics Suite), and 3) Testing demo surgery using Tx60 Stabuli.

The main aim of this project was to design a Computer Aided Diagnosis system which can detect mass/masses in mammograms to help the screening procedure. System architecture- Image → Candidate extraction → Feature extraction → Classification. Steps- 1) Candidate extraction- Histogram equalization → Multiple thresholding → Selection of n best threshold using mean and variance → Selection of desired threshold based on chosen threshold. 2) Feature extraction- Three types of features used are shape, texture (Haralick) and Intensity features. 3) Classification- Different type of classifiers were tested both individually and as a cascade of two or more to evaluate performance. At the end, the voting between Gaussian Naïve Bayes and Logistic regression was used where this voting is an AND combination of two classifiers.

This project proposes a discrete image reconstruction algorithm using parallel beam geometry where a set of X-ray beams are passed through the object of interest and intensity variations of the beams at input and output are measured. To reconstruct, filtered back projection is used which uses a 1D filter on the projection data before back projecting (2D or 3D) the data onto the image space.

VIZN application provides an environment where user will be able to test some performance related to the eyesight, Observing the evolution over time by the means of innovative user controls. In this project, all the tests were prepared in scientific way.

The purpose of this project was to design an Inverse Kinematic controller for Kinova Jaco2 robot following some given tasks. Task1- The control objective is position only without exploiting the redundancy. Task2- the control objective is given by both the position and the orientation. While the position needs to move according to the indications above, the orientation needs to be controlled such that it is kept constant at the initial value. Task3- The end-effector orientation needs to be changed. Task4- The redundancy needs to be exploited by maximizing the manipulability.

In this project, an adaptive technique was implemented to determine interest points or 3D objects based on Harris operator which is done by determining neighborhood of a vertex over which Harris response on that is calculated. Steps- 1) For each vertex of mesh, forming a region of interest and it’s K neighborhood rings where all the rings are set of points, 2) Finding harris response, 3) Getting initial interest points, 4) Getting final interest points (either by selection fraction method or by clustering), 5) 3D rendering, and 6) Designing graphical user interface.

The main aim of this project was improving the accuracy of automatic Left ventricular (LV) segmentation in short axis cardiac cine MR images. Steps- 1) Quantifying motion to determine an initial region of interest surrounding the heart, 2) Identifying potential 2D objects of interest using an intensity-based segmentation, 3)Assessing contraction/expansion, circularity, and proximity to lung tissue to score all objects of interest in terms of their likelihood of constituting part of the LV, and 4) Aggregating the objects into connected groups and construct the final LV blood pool volume and centroid.

In this project a simple face recognition system was designed based on a very small dataset of the training images. The two main steps of this project include data normalization and face recognition. After the normalization of the training data set, principle component analysis was incorporated. And a simple Graphical User Interface was designed.

The main goal of this project was to develop a visual inspection system. The following seven fault conditions were aimed to be detected- Bottle under filled, Bottle over filled, Bottle has label missing, Bottle has label but label printing was failed, Bottle label is not straight, Bottle cap is missing, and Bottle is deformed in some way. Background studies include extracting region of interest, computing mean gray level, binarizing any chosen region of interest with a particular thresholding, computing percentage of black pixels and comparing to normal bottle values, extracting red channel, edge detection and obtaining connected components & their bounding boxes, finding largest bounding box, comparing area, height & width to the normal. Depending on the fault condition different types of step were applied. And a simple graphical user interface was designed.

In this project, a gesture-controlled pick and place robot was proposed with a drive system. This design is wirelessly controllable using a hand module. Main purpose was to aid physically disabled people to manipulate an object as they wish. Moreover, will be useful in industrial works as it has the option of mobility, a trait that conventional pick and place robots do not have.