0 | Zakia Khatun

Graph Echo State Network for MRI-based Tendon Pathology Classification

Wed, 01 Jan 2025 00:00:00 +0000

Background and Objective:

Diagnosing tendon-related pathologies is crucial for early diagnosis and effective treatment planning, enabling timely interventions that can significantly improve patient outcomes.

Methods:

This study presents an end-to-end tendon pathology detection (classification) module utilizing a custom ankle MRI dataset comprising 76 subjects (45 healthy, 31 pathological). We propose a graph-based module that converts images into graph representations for classification. Superpixels are first generated by grouping pixels with similar intensity values, serving as the graph’s nodes, while edges connect neighboring superpixels to establish the graph structure. Next, a Graph Echo State Network (GESN) is employed for classification, leveraging its echo state property that eliminates the need for iterative backpropagation. This property produces rich graph embeddings which are fed into a linear readout layer, where weights and biases are learned using ridge regression with regularization. For baseline comparison, a non-graph-based module extracts radiomic features from both the entire image and individual superpixels, employing four traditional machine learning classifiers. We analyze and compare the performance of the graph-based and non-graph-based modules, using majority voting on slice-level predictions to generate subject-wise predictions.

Results:

Our baseline non-graph-based module achieved relatively better performance by extracting global radiomic features from entire images compared to local features derived from superpixels. In contrast, our graph-based module effectively integrates both local and global perspectives. The graph embeddings produced by the Graph Echo State Network (GESN) enhance data representation, resulting in a mean accuracy of 0.953 ± 0.013 and a mean sensitivity of 0.943 ± 0.035, both significantly surpassing the baseline performance. Additionally, hyperparameters such as the reservoir spectral radius and scaling factors had a notable impact on the outcomes of the graph-based classification.

Conclusions:

Our findings highlight the effectiveness of graph-based models, particularly GESN, in capturing meaningful representations and improving classification performance in tendon pathology detection.

To Design, Analyze, and Implement Approaches for Brain Tissue Segmentation

Thu, 16 Jan 2020 00:00:00 +0000

The main goal of this project was to develop tissue (WM, GM, and CSF) segmentation methods in brain MRI images. The 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 that hinders segmentation. System architecture used - 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.

Developing Computer Aided Algorithm for the Diagnosis in Histopathological Images to Classify Benign vs Malignant Using Deep Learning Approach

Thu, 09 Jan 2020 00:00:00 +0000

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 used - 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).

Developing Computer Aided Algorithm for the Diagnosis in Dermoscopic Images to Classify Melanoma vs All Other Types Using Deep Learning Approach

Wed, 08 Jan 2020 00:00:00 +0000

The main goal of this project was to design a computer-aided diagnosis system to classify dermoscopic images and determine 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).

Image Registration of Chest CT Volumes; 4DCT DIR-Lab Challenge

Tue, 07 Jan 2020 00:00:00 +0000

This project aims to register 3D CT lung images, which was tested on the 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.

Demonstration of Medical Robotics in Spine Surgery using Tx60 Staubli Robot

Sun, 05 Jan 2020 00:00:00 +0000

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.

Breast Mass Detection using Machine Learning Algorithms

Fri, 14 Jun 2019 00:00:00 +0000

The main aim of this project was to design a computer-aided diagnosis system that 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.

CT Reconstruction using Parallelization Strategy

Tue, 11 Jun 2019 00:00:00 +0000

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.

Vision Application (VIZN)

Sun, 09 Jun 2019 00:00:00 +0000

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

Designing an Inverse Kinematics Controller

Fri, 07 Jun 2019 00:00:00 +0000

The purpose of this project was to design an Inverse Kinematic controller for the Kinova Jaco2 robot to follow some given tasks.

Task 1 - The control objective is position only without exploiting the redundancy.

Task 2 - 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.

Task 3 - The end-effector orientation needs to be changed.

Task 4 - The redundancy needs to be exploited by maximizing the manipulability.

Interest Points Detection on 3D Meshes Using Harris Operator

Thu, 17 Jan 2019 00:00:00 +0000

In this project, an adaptive technique was implemented to determine interest points or 3D objects based on the Harris operator, which is done by determining the neighborhood of a vertex over which the Harris response 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.

Automatic MRI Cardiac Segmentation in Short Axis for Left Ventricular Endocardium

Wed, 16 Jan 2019 00:00:00 +0000

The main aim of this project was to improve 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.

Face Recognition using Principal Component Analysis

Tue, 15 Jan 2019 00:00:00 +0000

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. A simple Graphical User Interface was designed.

Automated Inspection at Soft Drink Bottling Plant

Sat, 12 Jan 2019 00:00:00 +0000

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 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 steps were applied. A simple graphical user interface was designed.