Medical Image Processing Projects using python mainly deals with the internal investigation processes of the medical images to recognize and treat the diseases. In general, biomedical images are a colossal collection of different patterns of human body organs/tissue like brain, muscle, bones, and brain. These internal parts of the human body are traced with the help of medical images such as CT and MRI. Though these medical images give the overall layout of the disease, it requires some advanced technologies to go through the image deeply for a better understanding of complex diseases. So, Medical Image Processing acts as the finest one-stop solution to tackle these issues.

In this page, we surely get an idea about the research need of medical image processing with their current research ideas!!!

Research Challenges in Medical Image Processing

Basically, Medical image processing faces several technical challenges because of the huge-scale data. So, it turns out to be a leading research area for scholars and the healthcare sector to find new technologies for overcoming complicated tasks.

In specific, some medical technologies may require 2D data, and others require 3D data like CT scan images.  Overall, the medical image has certain entities that are an identifier, metadata, and data elements which will vary for each one based on shape, intensity, size, and resolution. 

4 Emerging Research Areas in Medical Image Processing Projects

• Bioimaging – Nanoparticles
• Neuro-Imaging or Brain Imaging
• Digital Medical Image Management and Processing
• Virtual Reality Technology in 3D Medical Image Visualization

Can Python be used for Image Processing?

For effective image processing results, Medical Image Processing Projects Using Python is the best choice to be taken. Since python is flexible and scalable to support many kinds of image processing methodologies. For this purpose, it encloses several pre-defined libraries and functions to stand out as advanced image processing project tools.  Most importantly, python occupies the number one position in the developer’s priority list based on the following:

• Python is simple and easy to learn and code without compromising the software design decisions quality
• It is furnished with enormous number of libraries to solve large-scale programming issues
• As a result, it turns into a very effective OOP solution to grow dynamically for simplified coding
• Hence, it is an evergreen language in the research world which is more stable to tackle errors

Moreover, Python is widely used in numerous research fields due to its open-source capability and increasing fame of scientific programming language. The following points reveal the reasons behind its fast growth in the research world.

Highlights of Python for Medical Image Processing

• Python is an open-source software for handling and analyzing the medical image analysis using DL approaches
• Self-determining and Scalable data handling such as full or patch-wise and 2D or 3D images
• Seamless integration platform for current deep learning approaches like PyTorch and TensorFlow
• Adaptive and Simple change the framework for modeling
• Sophisticated functions for independent outcome assessments and report generation either in console or CSV files
• Simple to monitor the evolution of the training activities
• Includes domain-specific performance parameters for evaluating image regression, reconstruction and segmentation

Then, we have the latest python versions that scholars are majorly preferred for their research.

Latest Python Versions

These versions gain more attention in the implementation of the medical image processing projects using python.

• Python 3.8.2
• Python 3.8.3
• Python 3.8.4
• Python 3.8.5
• Python 3.8.6
• Python 3.8.7
• Python 3.8.8
• Python 3.8.9
• Python 3.8.10
• Python 3.9.0
• Python 3.9.1
• Python 3.9.2
• Python 3.9.3
• Python 3.9.4
• Python 3.9.5

At the present time, Python Package Index (PyPI) is considered to be the official database for third-party python software. Majorly, it encloses nearly 290,000 packages which comprise large-scale functionality with the following supports:

• Mobile App
• Multimedia
• Machine Learning
• Image Processing

From those huge packages, let us take Pymia Python Package as the sample one to discuss their functionalities. This package is used for deep learning-based Medical Imaging Applications. And, their operations are as follows:

• Creation – At first, it let the data package to form a dataset from unprocessed data (i.e., raw)
• Extraction – Next in order to process the neural network, it extracts the data from the dataset in any required format and pass that as the input to neural network
• Assembly – Then, it forecasts the neural network. And prior to the assessment, it will be collected back to original form  
• Evaluation – At last, it assess the forecasted neural network based on different performance metrics

As a matter of fact, we are equipped with a well-established professional research team to collect the best topics for Medical Image Processing Projects Using Python. Also, we are popular to crack the challenges that occur while processing medical images. Here, we have given the many kinds of medical images and we can use for the project.

7 Types of Medical Images

• X-rays
• Neuro MRI
• Ultrasound
• CT / PET
• Virtual Colonoscopy
• Computed Tomography (CT) Scan
• Magnetic Resonance Imaging (MRI)

How to collect Medical Image Processing Dataset?

For the purpose of handling these medical images, we are in need of a well-annotated dataset to practice them effectively. So, it is essential to identify the fitting dataset that is supposed to yield desired results. In truth, our team has sufficient knowledge to appropriately work with large-scale medical data for obtaining accurate parallel results in an efficient way.

In specific, an ideal dataset should meet the characteristics of Findable, Accessible, Interoperable, and Reusable (FAIR). Here, we have itemized some important data set that we are currently working on for Medical Image Processing Projects Using Python.

List of Important Medical Imaging Datasets

• NLM’s MedPix database
  • 12000 clinical patients annotated metadata along with 59,000 curated and indexed Medical Images
  • Intended Idea – Content / Context based Image Retrieval
• FITBIR
  • PET, MRI images for Traumatic Brain Injury (TBI)
  • Intended Idea – Advance Brain Tumor Segmentation
• NIH Database
  • 112,120 Chest X-ray images which is gathered from 30,000+ patients
  • Comprises Disease labels, clinical data and annotations
  • Intended Idea – COVID-19 Estimation
• ADNI
  • Genetics, PET / MRI images, CSF, Blood Biomarkers and Cognitive Tests
  • Intended Idea – Cognitive Analysis on Brain Images
• The Cancer Imaging Archive (TCIA)
  • Approximately NCI published 3.3 million cancer images
  • Cloud healthcare API consists of TCIA dataset
  • Intended Idea – Automatic MRI Data Noise Distributions Characterization
• OASIS
  • Data set of Cognitive and Clinical MRI Images for Alzheimer’s Disease and Normal Aging problems
  • Intended Idea – Reinforcement Learning based Alzheimer Disease Diagnosis
• STARE
  • 20 eye fundus images in 700 x 605 resolution
  • Intended Idea – Automated Diabetic Retinopathy Identification
• Facebase
  • 2454 patients clinical data that includes genotypes, 3D facial (surface and landmark co-ordinates) and 3D anthropometric measurements
  • Intended Idea – Face Recognition
• SICAS Medical Image Repository
  • 50 subjects Post mortem CT images
  • Statistical shape and surface models with clinical and genomics information
  • Intended Idea – Image Denoising
• Cancer Digital Slide Archive
  • Radiologycancer information and Digital pathology like Tissue specimens
  • Intended Idea – Ontology based Cognitive System Analysis
• Embodi3D
  • Real medical scans generates the 3D Printed Anatomic Model Library
  • Intended Idea – Image Degradation / Restoration

For your benefit, we have also listed the commonly used performance evaluation parameters in the field of Medical Image Processing. Since it only proves that your experimental results are better and efficient than the existing system.

Performance Metrics for Medical Image Processing

• False Negative Rate
• Precision
• Volume Similarity
• Accuracy
• Mutual Information
• False Negative
• Probabilistic Distance
• Fallout
• True Negative
• Cohen Kappa Co-efficient
• Area under Curve
• True Positive
• Hausdorff Distance
• Specificity
• Adjusted Rand Index (ARI)
• Surface Overlap
• Interclass / Intraclass Correlation
• False Positive
• Probabilistic Rand Index (PRI)
• Surface Dice Overlap
• Variation of Information (VoI)
• Volume Ratio
• Mean Squared Error
• Area Ratio
• F-measure
• Average Distance
• Root Mean Squared Error
• Mahalanobis Distance
• Sensitivity
• Mean Absolute Error
• Peak Signal-to-Noise Ratio
• Dice’s Similarity Co-efficient
• Co-efficient of Determination
• Normalized Root Mean Squared Error
• Structural Similarity Index Measure
• Jaccard’s Similarity / Index Co-efficient
• Global / Local Consistency Error (GCE / LCE)

Our developers are cooperative to suggest to you the suitable dataset and best image processing approaches that work well on handpicked image datasets. Hence, we are passionate about supporting you in all aspects of research medical image processing projects using python until you experience satisfaction in the research journey.