Medical Image Processing

Medical Image Processing 

What is Medical Image Processing?

 Medical imaging processing is the special subclass of digital image processing for enlightening the actual functioning of interior human body parts (organ/tissue/bone/muscle) by processing their medical images. 


What is medical imaging used for?

Medical imaging is defined as the process involved in acquiring the images of various internal human parts (i.e., medical images) through various digitalized medical equipment and technologies. Once the images or bio-signals are collected, then the

 image processing techniques like preprocessing, segmentation, enhancement, classification are applied 

to 

predict, monitor, and diagnosis the patient’s clinical state.



What kind of medical scans are there?

Types of Medical Imaging Scans

X-Ray

Ultrasound

Computerized Tomography (CT)

Magnetic Resonance Imaging (MRI)

Computerized Axial Tomography (CAT)


In recent days, the use of Artificial intelligence in medical image processing is extensively increasing. By integrating this technology with image processing, the medical disorders or abnormalities are easy to detect and assess efficiently. 


Artificial Intelligence Algorithms for Medical Image Processing

Deep Learning

Zero-shot Learning

Federated Learning

Transfer Learning

Self-Supervised Learning

Multimodal Deep Learning

Reinforcement Learning

Geometric Deep Learning

Distributed Machine Learning

Deep Learning Model Complexity

Weakly Supervised Learning

Incremental Deep Learning

Physics-based Machine Learning Model

Deep Neural Network (Optimization)


Latest Medical Image Processing Project

Project Title: Implementation of effective CNN based active contour model for Early Cancer Detection

Datasets: Wisconsin Dataset (Breast Cancer) and MICCAIBraTS 2013 (Brain Tumor)

Input Images: MRI or PET/CT scans Images

Images: MRI or PET/CT scans Images

 The main objective of this proposed work is to find the early-stage cancer tumor on medical images through advanced detection, segmentation, and classification image processing techniques. Also, it is intended to analyze the deep CNN-based Active Contour Model. In this proposed work, there are 5 phases that need to be performed to reach our aim. And they are given as follows,

Pre-processing

Tumor Segmentation

Feature Extraction

Feature Selection

Classification

Now, let’s see the task allocated for each phase and what are techniques and algorithms are used in these phases. As well, we can recognize how the flow of work is maintained throughout the execution.


Pre-Processing

Fast Bilateral Filter – To eliminate the noise and smoothen the image

Zig-Zag Order CLAHE – To improve the contrast of the image

Average Intensity Replacement

Patches Conversion

Tumor Segmentation


Convolutional Neural Network (CNN) – To segment the tumor part

Active Contour Models (ACMs) – To find false positive and false negative

Parameters: heterogeneous lesions, intensity inhomogeneous lesions and low contrast lesions

Deep Learning – To improve accuracy of segmentation

Feature Extraction

Fisher Vector Encoding (FVE) – To select the features for extraction

8-directions (3150, 2700, 2250, 1800, 1350, 900, 450 and 00)

Feature Selection

Particle Swarm Optimization (PSO) – To identify the optimal features

Classification

Support Vector Machine (SVM) – To classify the cancer tumor

At the end of implementation, we show that our proposed solution brings the best outcome in comparison to existing methodologies through the following performance metrics,

Computation Time

Classification Accuracy

Segmentation Accuracy




Compiled by

Ms Naresh kuwar 

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