How to choose the recent research medical image processing thesis topics? We provide the best guidance for medical imaging research work. Medical imaging is the process of inspecting the bio-medical images of human internal body organs/muscle/tissues to identify the actual patient’s health. In other words, it analyzes the medical images through intelligent image processing techniques for predicting and studying diseases.
This field offers an infinite number of imaging algorithms and methodologies to accurately assess clinical disorders. Also, it helps the physician to make effective decisions over diagnosing the disease. So, medical image processing is growing rapidly in the healthcare sector for several developments and applications.
In this page, you can find the end-to-end information on recent Medical Image Processing research with innovative ideas!!!
Latest topics in Medical Image Processing
- Advance Tumor Segmentation in MR Brain Images
- DL Method for Multi-Classification and Segmentation of Skin Lesion
- Intelligent Diabetic Retinopathy Detection and Analysis
- Improved Digestive System Disorder Recognition and Treatment
Ultimately, this technology enables us to analyze, enhance and recognize the Image in a better way to craft medical image processing thesis topics. Now, we can see about the research gaps where some research issues not solved yet or not attained efficient solutions so far.
Research Gaps in Medical Image Processing
- Software and Hardware
- Poor Lighting
- Object and Image Quality
- Real-world Environment
- Minimizing Quantization
- Dynamics in Image Sensor
- Performance gap in Image Processing
- Resampling and Reformatting Utilization
- Abnormal Lens Aperture Setting
- Image Digitization and Type Conversion
- Printer and Document Viewer Configuration
- Quality Maintenance in Image Compression
As a matter of fact, medical image processing is the extended support of image processing. This area is particularly designed for assisting medical-related fields. Majorly, it works on the below specified three primary goals for improved image visualization.
- High-resolution image display
- 3D Generation (from 2D slice)
- 3D Model (viewing, reconstruction, volumetric display)
- Enhanced processing of medical image
- Health disorder (analysis, identification, prediction, diagnosing)
- Object (search, recognition and segmentation)
- Improved image capture tools and methodologies
- Tools: X-ray, Ultrasound, MRI and more
- Technologies:
- Noise reduction
- Compression
- Data repositories
- Enhancement (contrast and resolution) and more
In addition, we have listed the best result-generating algorithms and techniques used for common operations in medical image processing using matlab. Accordingly, it delivers an enhanced diagnostic System for solving major research issues helps to formulate medical image processing thesis topics.
What are the algorithms used in medical image processing?
- Methodologies for Segmenting Image
- Watershed
- GrowCut
- Smart Region Growing
- Fast Random Walker
- Techniques for Resizing Image
- Liquid Rescaling (Seam carving)
- Content-Aware Image Resizing (CAIR)
- Feature Matching and Detection Approaches
- Canny Edge Detector (CED) Algorithm
- Linear based Generalised Hough Transform (Linear – GHT)
- Circular Hough Transform (CHT)
- Marr–Hildreth’s Edge Detection Algorithm
- SIFT and SURF
- Improvement of Image Contrast
- Dynamic Histogram Equalization (based on nearer pixel)
- Adaptive Histogram Equalization
- Methods for De-blurring process in an image
- Lucy- Richardson deconvolution Procedure
- Blind deconvolution Procedure
- Labeling Connected Components
- Difference-Map Algorithm (by Veit Elser)
- Digital Half-toning and Dithering Techniques
- Parallel Error Diffusion
- Other Dithering Algorithms (Ordered, Riemersma and Floyd–Steinberg)
Most probably, in many healthcare applications, the images are renovated for better interpretation from their original degradation features. At this time, it is necessary to assess the Image’s quantitative image quality. So, the original image is also required to evaluate the reconstructed Image. Below, we have specified few improved diagnostic approaches for medical image processing.
Current trends in Medical Image Processing
- Polyp Detection
- Input: Colonoscopy Video or Images
- Aim: Polyp Auto-detection
- Melanoma Detection
- Input: Dermoscopic images
- Aim: Skin lesions Auto-detection and Auto-classification
- Left Ventricular Ejection Fraction
- Input: Cine-MRI images
- Aim: Left Ventricle Auto-segmentation and LVEEF Assessment
- 3D Airway Volume Analysis
- Input: MRI images
- Aim: Upper airway Auto-Volumetric-Segmentation
- Breast Cancer Detection
- Input: Microscopy images
- Aim: Breast Cancer Auto-Classification
- 3D Cephalometric Analysis
- Input: MRI images
- Aim: Cephalometric landmarks Auto-Detection
- 2D X-ray to 3D Surface Development
- Input: X-Ray images
- Aim: 3D Surface image construction
- Liver Segmentation
- Input: Volumetric MRI and CT images
- Aim: Liver Auto-Segmentation
- Brain Segmentation
- Input: MRI images
- Aim: Brain Auto-Segmentation
- Retinal Disease Detection
- Input: Optical CT images
- Aim: Various Kind Fluids Auto-Segmentation and Detection
- Lung Segmentation
- Input: CT images
- Aim: Lung Auto-Segmentation
- Nerve Segmentation
- Input: Ultrasound images
- Aim: Nerve Auto-Segmentation
- Liver Tumor Detection
- Input: Volumetric MRI and CT images
- Aim: Liver Cancer Auto-Segmentation
- Brain Tumor Segmentation
- Input: MRI images
- Aim: Brain Tumor Auto-Segmentation
- Lung Cancer Segmentation
- Input: Chest CT images
- Aim: Tumor Auto-Detection and Segmentation
- Cell Tracking
- Input: Microscopic images
- Aim: Biological Cell Auto-Tracking
- Pulmonary blood-vessel Segmentation
- Input: Chest CT images
- Aim: Pulmonary blood-vessels Auto-Segmentation
- Diabetic Retionopathy
- Input: Retinal fundus images
- Aim: Diabetic Retinopathy Auto-Detection and Grading
Medical Image Processing Frameworks and Libraries
In theoretical aspects, it says everything is possible in developing image processing applications from scratch. But in the case of real-time executions of Medical Image Processing Thesis Topics, we need to employ suitable frameworks and libraries. Selection of the best development platform will make your tasks easy to implement. Hence, we have given a list of frameworks and libraries for medical image processing projects,
- EmguCV
- Support “wrapper functtion” to incorporate .Net framework languages along with OpenCV for image processing of image
- Functionality:
- OS – iOS, Linux, Windows, Android and Mac OS
- Languages – VC ++, C #, IronPython, VB, etc.
- Development Tools – Unity, Visual Studio and Xamarin Studio
- Caffe
- Offer a sophisticated learning platform for dealing with CV and huge-scale industrial applications
- Functionality:
- Simple Coding
- Ultra-Fast Functions
- Modeling and Optimization
- BLOBs based Data Computation
- Dynamic Switching from CPU to GPU
- OpenCV
- Provide open infrastructure with massive in-built predefined functions and algorithms. Also, it enables to develop real-time applications
- Functionality:
- Java API
- Android version
- 3D Display and Search
- Fundamental DS algorithms
- Input and output (videos and images)
- CV and DIP Techniques
- Continuous integration (CI) and Optic-flow
- CUDA Programming
- Cross platform with embedded Test environment
- VXL
- Software with large number of libraries to create home-working solutions and end-point solutions for industries
- Functionality:
- 3D images
- Structure Restoration
- Extensive Image Manipulation
- Designing GUI
- Geometric CV (curves, elements and points)
- Topology
- GDAL
- Set of libraries which include vector types and raster to read and manipulate geo-spatial data
- Functionality:
- Data Re-Projection
- Mosaics and Shapefile Creation
- Easy to obtain Raster Data
- Different Format Conversion
- MIScnn
- Open-source technology with API to build the 2D or 3D medical images for segmentation process
- Functionality:
- Input Output
- Cross Validation
- Segmentation
- Patch Analysis
- Automated Evaluation
- Preliminary Processing
- Tracking
- Support JavaScript library for performing CV image processing operations( tracking, recognizing and segmenting images)
- Functionality:
- Support HTML5
- Color object Tracking
- Lightweight Kernel
- Easy Face Detection
- PyTorch
- Intended to develop DL models for both research and industrial purposes through ML methods
- Functionality:
- Distributed Learning
- Automatic Differentiation
- Framework for High Performance
- Support Cloudsim based System
- Exploration-to-Production Transition
- Enriched Libraries and Technologies
- TensorFlow
- Designed to enhance the human interpretation through building and training ANN using learning approaches
- Functionality:
- Fast-Iteration
- Interactive Log Viewer
- Parallel Processing over Multi-Processors
- Graph and Model Optimization
- Designing Own Logging Service
- Computation of Arithmetic Operations (tensors)
- Easy-Debugging
- WebGazer
- Includes eye tracking library for real-time monitoring and tracking of visitors through webcams
- Functionality:
- Javascript Integration
- Client-side Processing
- Self-calibration model
- Forecast the Different Views
- Marvin
- Launched for providing well-established environment for handling image / video processing and analysis
- Functionality:
- Fractals Generation
- Moving Object Detection
- Extraction of Video Frames
- Support Plugins through GUI
- Video Filtering and Processing
- Multithreading Image Processing
- Kornia
- It is made up of differentiable practices and archives for working with basic CV and DL models
- Functionality:
- Epipolar Geometry
- Color Correction
- Depth Estimation
- Low-level image processing
- Feature and Edge recognition
- Image Conversion and Filtering
Medical Image Processing Datasets
In medical image processing, we able to construct the ML model in the absence of the data. When we use more labeled data for training large datasets, it is very important for further processing in any kind of application using project dip. As are a result, it yields a better outcome than the other common approaches. For your information, we have given you a widely used image dataset in many practical implementations:
- ImageNet-A
- 7.5 Thousand images (Visual illusions in Nature where the ANN is incorrectly classified)
- Prediction of objects in 3% accuracy
- Purpose: To retain the ANN stability in ambiguous images
- Diversity in Faces
- 1 million people face images (differs in physical features and bio-data)
- Purpose: To minimize the unfairness in face recognition
- FaceForencis
- Half a million images (using DeepFakes, Face2Face and FaceSwap techniques)
- Use 1 falsification = 1000 videos sequence
- Purpose: To identify unreal videos and pictures
Performance Analysis in Medical Image Processing
In the code development of the Medical Image Processing Thesis Topics, the performance evaluation process is more significant compare to others since it helps to assess the overall efficiency of the proposed System by relating it with previously used image processing technologies. Here, the following metrics measure how the Image is restored from the degradation.
- Histogram Analysis
- Global Consistency Error (GCE)
- Probabilistic Rand Index (PRI)
- Mean Structure Similarity Index Map (MSSIM)
- Variation of Information (VOI)
Further, if you want to know more findings on current Medical Image Processing Thesis Topics, then communicate with us. We surely let you know the important updates on recent research activities on medical image processing.