Blood Cell Segmentation via Custom Color Space Transformation
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Solution Overview
Problem
Current cell segmentation techniques in disease diagnostics, particularly for blood cells, face challenges in accurately differentiating between white and red blood cells due to inadequate color spaces and assumptions that do not account for the unique characteristics of blood cells, leading to poor segmentation results and difficulties in early disease detection.
Innovation Solution
A method employing a robust and discriminative color space transformation for blood smear images, combined with feature-based segmentation and classification using shape, color, texture, and invariant information, to enhance the differentiation between cells and classify abnormal cell characteristics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If intensity thresholding is used for cell segmentation, then the process is simple and fast, but segmentation accuracy deteriorates when illumination and color representation vary
Solution Approach 1:
The patent transforms the image from RGB color space to a custom color space defined by specific linear combinations of R, G, and B channels. This parameter transformation creates new intensity metrics that are invariant to illumination variations, thereby maintaining segmentation accuracy across different imaging conditions while keeping the thresholding approach simple.
Solution Approach 2:
The patent introduces an intermediate color space transformation as a mediator between the raw RGB image and the final segmentation. This intermediate representation (with channels like R-G, B-R, and R+B) serves as a bridge that preserves cell characteristics while eliminating illumination-dependent variations, enabling accurate thresholding.
2Measurement precision
If feature-based segmentation using filtering is employed, then edge detection improves, but perfect cell segmentation cannot be achieved without further enhancements
Solution Approach 1:
The patent combines multiple segmentation approaches into a unified system. It merges the color space transformation with thresholding, and further integrates filtering operations and morphological processing. This combination allows the system to achieve perfect cell segmentation by complementing the strengths of each method while mitigating their individual weaknesses.
3Adaptability or versatility
If global information is used for cell segmentation, then the entire image is considered, but special features of individual blood cells are not adequately addressed
Solution Approach 1:
The patent first segments individual cells from the background using thresholding on the transformed color space, then performs separate analysis on each segmented cell region. This two-stage approach allows global image processing to isolate cells, followed by local analysis that captures special features of individual cell types, including the presence of two different colors within the same cell.
Data Source
AI summary
This invention describes methods and systems for use of computer vision systems for classification of biological cells as an aid in disease diagnostics. More particularly the present invention describes a process comprising employing a robust and discriminative color space which will help provide segmentation of the cells; employing a segmentation algorithm, such as a feature-based level set, that will be able to segment the cells using a different k-phase-segmentation process, which detect for example, if a while blood cell occurs for segmenting the internal components of the cell robustly; employing a combination of different type of features including shape, texture, and invariant information, and employing a classification step to associate abnormal cell characteristics with disease states.