Necrotic Cell Region Detection Using Texture and Luminance Features
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Solution Overview
Problem
Current cell image analysis methods struggle to accurately distinguish necrotic cell regions from living cells, which can lead to incorrect cell counting and activity measurements due to the similarity in morphological features between necrotic and living cells.
Innovation Solution
A necrotic cell region detection apparatus and method that acquires cell images over time, segments them into uniform regions, separates the images into low-band and high-band components, calculates texture feature values and luminance averages, and uses a feature space to judge whether each region is necrotic, employing techniques like co-occurrence matrices and discriminant analysis for accurate classification.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If morphological feature values based on contour shape deviation are used to distinguish living cells from dead cells, then the measurement process is simplified, but the detection precision deteriorates because necrotic regions cannot be accurately distinguished from living cells
Solution Approach 1:
The patent segments the cell image into multiple regions with uniform local imaging properties, then analyzes each region separately using texture and luminance features. This segmentation approach enables accurate differentiation of necrotic regions from living cells while maintaining automated processing efficiency.
Solution Approach 2:
The patent transitions from analyzing only contour shape (2D boundary) to analyzing texture and luminance features across multiple image bands (adding dimensional depth to feature space). By forming a feature space with texture feature values and luminance average values, the system achieves better discrimination between necrotic and living cells.
2Productivity
If simple contour-based morphological analysis is used, then processing speed is improved, but detection precision deteriorates due to inability to distinguish necrotic regions from living cells
Solution Approach 1:
The patent performs preliminary segmentation of the cell image into regions with uniform local imaging properties before feature extraction. This preliminary action organizes the data structure, enabling efficient subsequent processing with texture and luminance analysis while maintaining high processing speed.
Solution Approach 2:
The patent extracts specific texture feature values from high-band images and luminance average values from low-band images for each segmented region. By extracting only the relevant features needed for necrotic region detection, the system achieves accurate classification without unnecessary computational overhead.
3Productivity
If automated image processing is applied to measure cell activity, then productivity is improved, but reliability deteriorates due to misclassification of necrotic regions as living cells
Solution Approach 1:
The patent forms a two-dimensional feature space using texture feature values (from high-band images) and luminance average values (from low-band images). This dimensional expansion in feature space enables more reliable discrimination between necrotic and living cells, preventing misclassification while maintaining automated processing.
Solution Approach 2:
The patent changes the parameters used for cell classification from simple contour deviation to a combination of texture feature values and luminance average values. This parameter transformation fundamentally improves the reliability of automated cell activity measurement by providing more discriminative features for classification.
Data Source
AI summary
A necrotic cell region detection apparatus includes an image acquiring unit, a segmentation unit, a band separate unit, a feature value calculating unit, a luminance calculating unit, and a judging unit. The image acquiring unit acquires a cell image. The segmentation unit divides the cell image into multiple regions so that a local imaging properties. The band separate unit separates a low-band image and a high-band image. The judging unit forms a feature space composed of the texture feature value calculated by the feature value calculating unit and the luminance average value calculated by the luminance calculating unit, and judges a region formed by necrotic cell in the feature space.