Fuzzy-C-Means Clustering Based Segmentation and CNN-Classification for Accurate Segmentation of Lung Nodules

Area of Science:

• Medical Imaging
• Computer-Aided Diagnosis
• Artificial Intelligence in Medicine

Background:

• Accurate segmentation of lung regions in CT scans is critical for reliable computer-aided disease diagnostics.
• Conventional methods like Markov–Gibbs Random Field (MGRF) have limitations in segmenting complex multimodal lung CT images.
• The need for precise identification of abnormal and healthy lung tissues necessitates advanced segmentation techniques.

Purpose of the Study:

• To propose novel methods for segmenting multimodal grayscale lung CT scans.
• To accurately distinguish between normal and abnormal lung tissues for improved computer-aided diagnostics.
• To evaluate the performance of proposed segmentation techniques against conventional MGRF models.

Main Methods:

• Processing of chest CT scan stacks using novel segmentation approaches.
• Implementation of Fuzzy C-Means Clustering (FCM) for empirical dispersion computation and precise boundary identification.
• Application of a Convolutional Neural Network (CNN) classifier for distinguishing normal from abnormal lung tissue.

Main Results:

• The proposed FCM and CNN-based segmentation methods demonstrate precise segmentation of complex multimodal medical images.
• Comparative analysis shows superior performance of the proposed methods over the conventional MGRF model.
• Experimental evaluation using the Interstitial Lung Disease (ILD) database validates the effectiveness of the approach.

Conclusions:

• The developed Fuzzy C-Means Clustering and Convolutional Neural Network approach offers accurate segmentation of lung CT scans.
• This method effectively differentiates normal and abnormal lung tissues, enhancing computer-aided disease diagnostic capabilities.
• The study highlights the potential of advanced AI techniques for precise medical image analysis in lung disease detection.