Local segmentation of biomedical images

Area of Science:

• Biomedical Imaging
• Image Processing
• Computer Vision

Background:

• Accurate segmentation of biomedical images is crucial for diagnosis and analysis.
• Existing methods may struggle with small or localized objects of interest.
• Gray level image segmentation involves classifying pixel values into distinct groups.

Purpose of the Study:

• To present a new algorithm for local segmentation of biomedical images.
• To introduce a method suitable for segmenting small and localized objects.
• To demonstrate the effectiveness of a quantization-based approach for image segmentation.

Main Methods:

• A small region is selected based on dispersion measurement of local gray values.
• Segmentation is performed using an algorithm based on signal quantization principles.
• An N-level threshold selection method is employed, analogous to designing an N-level optimal quantizer.

Main Results:

• The algorithm successfully segments localized regions in biomedical images.
• Experimental results demonstrate the efficacy of the proposed segmentation scheme.
• The quantization approach provides a robust method for gray level image segmentation.

Conclusions:

• The developed algorithm offers an effective solution for local biomedical image segmentation.
• This approach is particularly beneficial for applications involving small, localized structures.
• The quantization-based method shows promise for advancing biomedical image analysis.