The Next Big Thing in Medical Image Processing Project Ideas, everything that you must need to know is explained detail in this page. 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. Further, it enfolds several 2D and 3D imaging techniques as image acquisition, transmission, visualization, and storage.

Also, medical imaging techniques are encapsulated with numerous advantages such as scalability, communication, adaptability, flexibility, wide-data storage and etc. Overall, medical image processing is the mechanism to manage and analyze medical images through computing technologies.

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.

Here, we have given some medical applications for your information.

• Multi-sensor Thoracic Data Fusion for Datasets   
• 3D Ultrasound Imaging by Spatial Compound    
• 3D MRI or CT Scan Images Fusion
• Accurate Clinical Adaptability, Forecasting and Reasoning
• Memory and Time Efficient 3D Morphological Segmentation
• 3D Optimal Path Selection using Skeletonization Approach for Brain Blood Vessels
• Early Tumor Prediction and Diagnosis (Response to Radiation)    
• Inhomogeneities Correction of MRI Images (Intensity, Retrospective and Multiplicative)

What kind of medical scans are there?

Now, we can see about the different types of medical scans and imaging for creating or capturing the human body parts with their structure for clinical analysis. Below, we have given some commonly used medical image scanners.

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 because of its sensitivity and accuracy characteristics. By integrating this technology with image processing, the medical disorders or abnormalities are easy to detect and assess efficiently. Here, we have given some artificial intelligence methods/procedures for your reference. 

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)

Based on the requirements of the handpicked Medical Image Processing Project Ideas, the image processing algorithms and operations will vary. For instance: if you deal with medical imaging, then you have to prefer 3D registration, reconstruction, and segmentation. Similarly, if you work with automation, then segmentation is a very essential and primary operation to be handled.  Likewise, if you carry out multimedia, video, or image, quality enhancement is very important. Here, we have given the few latest medical image processing research ideas for your project title.

Top 4 Medical Image Processing Project Ideas

• Automatic Human Face Recognition and Segmentation
• Deep Learning based Covid19 Severity Detection and Classification
• Enhanced Clinical Disorder Diagnosis by Medical Image Processing
• Hand Written Text Recognition and Steganography in Military Application

Next, we can see some majorly utilized functions from Matlab toolboxes for processing medical images. Here, we have presented you few lists of filtering functions of various types.  Each filter has different purposes of performing like noise-reducing, edge detecting, classifying, image sharpening and enhancing, etc.

Matlab Image Processing Toolbox

  This toolbox has the different filter types,

• Gaussian – Gaussian low‐pass filter (for sharpening image)
• Average – Averaging filter (for smoothing image)
• Log – Laplacian of Gaussian filter (for minimizing noise sensitivity)
• Prewitt – Prewitt filter (for detecting edge in both vertical and horizontal areas)
• Disk – Pillbox – circular averaging filter (for blurring image impact)
• Laplacian – 2D Laplacian operator (for sharpening edge regions)
• Sobel – Sobel operator / filter (for edge detection)
• Motion – Linear camera motion (for determining vertical and horizontal motions)

For more clarification, in this section, we have explained the primary use cases of the Matlab toolbox. Majorly, it will apply mathematical functions on input images for the purpose of image filtering, preprocessing, segmenting, feature extracting, registering, and displaying.

Due to its enriched capabilities and functions, it gains the attention of scholars who are interested in doing their research in medical image processing. Therefore, more than half of the researchers are utilizing MATLAB as their development tool. Also, medical imaging is differing from natural image processing and computer vision.

Even though image processing furnishes extensive tools, it provides the facility to design its own algorithm or import other external libraries. In addition, there is so much independent software to perform specific image processing tasks. Our technical professionals have years of experience in handling all these technologies by developing an infinite number of Medical Image Processing Project Ideas. In the case of a challenging research topic, we create our own algorithm or pseudo-code to tackle the problems effortlessly. In some other cases, we implement hybrid methods to crack the complex research questions.

  Moreover, this tool supports large-scale data for medical image analysis. For instance: through wearable devices, we can monitor the patient’s health by collecting digital health records. Further, we can apply suitable algorithms over it based on application needs for improving the current health state and controlling future health risks. For your awareness, we have itemized some widely used measures that Matlab can perform for medical image analysis.

Medical Image Analysis with MATLAB

With MATLAB, you can:

• Efficiently manage DICOM images (load, process, parse and visualize)
• Apply volume rendering and volume visualizing approaches on 2D/3D Images
• Simplify the multi-label classification process by employing deep learning mechanisms
• Implement multi-resolution techniques on large-scale high-resolution images
• Reduce the complication of processing medical images using sophisticated methods / algorithms

For illustrative purposes, we can see how the 3D volumetric data is analyzed. At first, we can import the Volume Viewer app in MATLAB. Then, load the MR data of the human brain into the volume viewer app. Next, perform a thorough analysis of data through various processing techniques. As a result, you can detect the exact location of the brain tumor with its type. Similar to the volume viewer app, we have other task specified toolboxes, and a few are given below,

Matlab Toolboxes for Medical Image Processing

• Matlab Toolboxes for Medical Image Processing
• Statistics and Machine Learning Toolbox
• Deep Learning Toolbox
• Image Processing Toolbox

Latest Medical Image Processing Project

Then our development team has given how the medical image processing project ideas is implemented in real, how we divide the project into modules, how we integrate the modules with each other and how the overall performance of the system is measured through the below sample 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 (315^0, 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

In addition, our research team has given you the list of current Medical Image Processing Project Ideas that we are currently working on. All these topics are based on the demand of the scholars. Furthermore, we have massive interesting topics in recent research areas in the digital image processing domain. Make contact with us to know all the latest developments in your interesting research area.

Medical Image Processing Project Titles

• An effective mechanism for Low-Bit Hardware Implementation of DWT intended for 3D Medical Images Processing
• A new process of Exploiting Epistemic Uncertainty based on Anatomy Segmentation aimed at Anomaly Detection in Retinal OCT
• An innovative mechanism for Adaptive Medical Image Deep Color Perception Algorithm
• Fresh process of High-Resolution Encoder–Decoder Networks intended for Low-Contrast Medical Image Segmentation scheme
• An effective method for Extending Hybrid Surgical Guidance Concept With Freehand Fluorescence Tomography
• A new-fangled method for Flexible Prediction of CT Images From MRI Data Through Improved Neighborhood Anchored Regression used for PET Attenuation Correction
• Inventive progression for Extended Visual Cryptography Technique used for Medical Image Security
• An effectual performance for Skin Surface Detection in 3D Optoacoustic Mesoscopy Based on Dynamic Programming
• New-fangled mechanism for Boundary-Weighted Domain Adaptive Neural Network intended for Prostate MR Image Segmentation
• An inventive thing of  Secure and Privacy-Preserving Technique Based on Contrast-Enhancement Reversible Data Hiding and Plaintext Encryption aimed at Medical Images
• The firsthand function of Dual-Discriminator Conditional Generative Adversarial Network used for Multi-Resolution Image Fusion
• An innovative process of Wavelet-Based Enhanced Medical Image Super Resolution
• The novel approach for Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
• A new system for Reducing the Hausdorff Distance in Medical Image Segmentation With CNNs
• An innovative methodology for Head CT Image Convolution Feature Segmentation and Morphological Filtering aimed at Densely Matching Points of IoTs
• The novel method for Spectrum Estimation-Guided Iterative Reconstruction Algorithm intended for Dual Energy CT
• A creative Image Recovery via Transform Learning and Low-Rank Modeling based on Power of Complementary Regularizers
• An innovative mechanism for Quality Control of Microwave Equipment used for Tissue Imaging
• A novel technique for Sparse Domain Gaussianization for Multi-variate Statistical Modeling of Retinal OCT Images
• Implementation of effective CNN based active contour model for Early Cancer Detection

On the whole, we are glad to inform you that we will support you throughout your research career until we meet your research expectation from us. Our supporting medical image processing project ideas services are area identification, topic selection, code execution, manuscript writing, and publication.