Related Experiment Video
Updated: Mar 1, 2026
High-resolution In Vivo Manual Segmentation Protocol for Human Hippocampal Subfields Using 3T Magnetic Resonance Imaging
Published on: November 10, 2015
Modified cuckoo search algorithm in microscopic image segmentation of hippocampus
Shouvik Chakraborty1, Sankhadeep Chatterjee2, Nilanjan Dey3
1Department of Computer Science and Engineering, University of Kalyani, Kolkata, India.
Insights
A novel cuckoo search method enhances microscopic cell segmentation in brain images. The Tsallis entropy approach achieved superior performance in accuracy, while Kapur
Area of Science:
- * Biomedical image analysis
- * Computational neuroscience
- * Medical diagnostics
Background:
- * Microscopic image analysis is crucial for cell identification and counting in medicine.
- * Accurate segmentation is vital for disease diagnosis but challenging due to image complexities.
- * Existing methods struggle with weak correlations and ambiguous segments.
Purpose of the Study:
- * To propose a novel method for accurate cell segmentation and identification in microscopic images.
- * To apply a modified cuckoo search algorithm with Levy flight for improved segmentation.
- * To evaluate segmentation performance using various objective functions and metrics.
Main Methods:
- * Pre-processing of light microscope images of rat hippocampus.
- * Application of a modified cuckoo search (CS) algorithm incorporating McCulloch's method for Levy flight.
- * Utilization of Otsu's method, Kapur entropy, and Tsallis entropy as objective functions for segmentation.
- * Direct application of the method on color images.
Main Results:
- * The modified CS algorithm effectively segmented brain cells in microscopic images.
- * Kapur's entropy with modified CS demonstrated the fastest computation time.
- * Tsallis entropy with optimized multi-threshold levels yielded the highest Peak Signal to Noise Ratio (PSNR), indicating superior accuracy.
- * Performance was validated using PSNR, Mean Square Error, Feature Similarity Index, and CPU time.
Conclusions:
- * The proposed cuckoo search-based method offers an effective approach for microscopic cell segmentation.
- * Tsallis entropy provides superior segmentation accuracy, while Kapur's entropy offers computational efficiency.
- * The method holds potential for precise cell identification and counting in medical image analysis.
Abstract:
Microscopic image analysis is one of the challenging tasks due to the presence of weak correlation and different segments of interest that may lead to ambiguity. It is also valuable in foremost meadows of technology and medicine. Identification and counting of cells play a vital role in features extraction to diagnose particular diseases precisely. Different segments should be identified accurately in order to identify and to count cells in a microscope image. Consequently, in the current work, a novel method for cell segmentation and identification has been proposed that incorporated marking cells. Thus, a novel method based on cuckoo search after pre-processing step is employed. The method is developed and evaluated on light microscope images of rats' hippocampus which used as a sample for the brain cells. The proposed method can be applied on the color images directly. The proposed approach incorporates the McCulloch's method for lévy flight production in cuckoo search (CS) algorithm. Several objective functions, namely Otsu's method, Kapur entropy and Tsallis entropy are used for segmentation. In the cuckoo search process, the Otsu's between class variance, Kapur's entropy and Tsallis entropy are employed as the objective functions to be optimized. Experimental results are validated by different metrics, namely the peak signal to noise ratio (PSNR), mean square error, feature similarity index and CPU running time for all the test cases. The experimental results established that the Kapur's entropy segmentation method based on the modified CS required the least computational time compared to Otsu's between-class variance segmentation method and the Tsallis entropy segmentation method. Nevertheless, Tsallis entropy method with optimized multi-threshold levels achieved superior performance compared to the other two segmentation methods in terms of the PSNR.
