Segmentation and Classification of Heart Angiographic Images Using Machine Learning Techniques
Abdullah1, Muhammad Hameed Siddiqi2, Yousef Salamah Alhwaiti2
1Department of Computer and Software Technology, University of Swat, KPK, Mingora, Pakistan.
Insights
This study introduces an automated method for segmenting and classifying heart blood vessels in angiographic images. This computer-assisted approach aims to improve accuracy and speed up the diagnosis of heart diseases.
Area of Science:
- Medical Imaging
- Cardiology
- Computer-Assisted Diagnosis
Background:
- Heart angiography is crucial for diagnosing heart vessel abnormalities but is time-consuming and prone to errors.
- Accurate segmentation and classification of heart blood vessels are vital for timely disease detection.
Purpose of the Study:
- To develop a computer-assisted system for localizing human heart blood vessels in angiographic images.
- To enhance the accuracy and efficiency of diagnosing heart-related diseases through automated analysis.
Main Methods:
- Proposed a multiclass ensemble classification mechanism for heart blood vessel analysis.
- Implemented automated segmentation of heart blood vessels followed by feature extraction (texture, statistical, geometrical).
- Classified vessels into four categories: normal, block, narrow, and blood flow-reduced.
Main Results:
- The proposed computer-assisted approach successfully segmented and classified heart blood vessels.
- Extracted low-level features like texture, statistical, and geometrical properties for accurate analysis.
- Achieved high accuracy in categorizing vessels into normal, block, narrow, and blood flow-reduced states.
Conclusions:
- The developed system offers a useful, accurate, and time-saving environment for cardiologists.
- Automated segmentation and classification significantly improve the truthfulness and speed of diagnosing heart illnesses.
- This computer-assisted diagnosis system aids in the early detection and management of cardiovascular diseases.
Abstract:
Heart angiography is a test in which the concerned medical specialist identifies the abnormality in heart vessels. This type of diagnosis takes a lot of time by the concerned physician. In our proposed method, we segmented the interested regions of heart vessels and then classified. Segmentation and classification of heart angiography provides significant information for the physician as well as patient. Contradictorily, in the mention domain of heart angiography, the charge is prone to error, phase overwhelming, and thought-provoking task for the physician (heart specialist). An automatic segmentation and classification of heart blood vessels descriptions can improve the truthfulness and speed up the finding of heart illnesses. In this work, we recommend a computer-assisted conclusion arrangement for the localization of human heart blood vessels within heart angiographic imageries by using multiclass ensemble classification mechanism. In the proposed work, the heart blood vessels will be first segmented, and the various features according to accuracy have been extracted. Low-level features such as texture, statistical, and geometrical features were extracted in human heart blood vessels. At last, in the proposed framework, heart blood vessels have been categorized in their four respective classes including normal, block, narrow, and blood flow-reduced vessels. The proposed approach has achieved best result which provides very useful, easy, accurate, and time-saving environment to cardiologists for the diagnosis of heart-related diseases.
