An adaptable threshold detector

Pai, Pei-Yan; Chang, Chin‐Chen; Chan, Yung-Kuan; Tsai, Meng‐Hsiun

doi:10.1016/j.ins.2010.12.007

Cited by 14 publications

(5 citation statements)

References 22 publications

(20 reference statements)

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“…Furthermore, via the quantity of the data in each class, OTM unites the variances of the data in all the divided classes into one variance to indicate the discrepancy of all the data within classes. However, if the variances or the quantities of data among different classes are quite different, OTM cannot provide suitable thresholds to effectively divide the data into classes [6]. In this paper, we used the variance and the quantity of the data in each class to compute the optimal thresholds.…”

Section: Leukocyte Nuclei Segmentation Methodsmentioning

confidence: 99%

“…Using GBPD, N chromosomes, each with p + m + n binary bits, were first randomly generated. To evolve the best solution, the genetic algorithm repeatedly executes mutation, crossover, and selection operations [6] until the relative fitness of the reserved chromosomes is very similar.…”

Section: Genetic-based Parameter Detectormentioning

confidence: 99%

“…A chromosome, in essence a character string or a binary string, represents the coordinate of an individual in the search space. A gene is a subsection of a chromosome that encodes the value of a single parameter being optimized [6].…”

Section: Genetic-based Parameter Detectormentioning

confidence: 99%

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An adaptive leukocyte nucleus segmentation using genetic algorithm

Huang

Hung

Chan

2012

2012 International Symposium on Intelligent Signal Processing and Communications Systems

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Leukocyte segmentation and location detection is the most important preprocessing step for further recognition. In this paper, an adaptive leukocyte segmentation method is proposed. Two kinds of color spaces are considered to enhance the nuclei. With the combined color space, the variety of stain and light condition can be avoided. In order to deal with different sizes of images, an adaptive segmentation method based on genetic algorithm is proposed. The experimental result shows that we can obtain promised segmental results even apply on different color tone and size of smear images.

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Section: Leukocyte Nuclei Segmentation Methodsmentioning

confidence: 99%

Section: Genetic-based Parameter Detectormentioning

confidence: 99%

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An adaptive leukocyte nucleus segmentation using genetic algorithm

Huang

Hung

Chan

2012

2012 International Symposium on Intelligent Signal Processing and Communications Systems

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Section: Genetic-based Parameter Selectormentioning

confidence: 99%

“…To develop the best solution, the genetic algorithm repeatedly executes the three operations mutation, crossover, and selection [15] until the fitness of the reserved chromosomes are similar to one another.…”

Section: Genetic-based Parameter Selectormentioning

confidence: 99%

Leukocyte nucleus segmentation and recognition in color blood-smear images

Huang

Hung

2012

2012 IEEE International Instrumentation and Measurement Technology Conference Proceedings

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In this paper, a leukocyte segmentation and recognition method is proposed for leukocyte differential counting. In general, leukocytes are usually manually classified in laboratories by using microscopes. It is a painstaking and subjective task for biologists. An automatic method is essential to reduce the overhead for biologists. The nuclei are used to identify five types of leukocyte in this paper. The leukocyte cell nucleus enhancer is proposed to segment the region we are interested in by enhancing the region of the leukocyte nucleus and suppressing the other region of the blood smear images. In the recognition steps, we reduce features by principle component analysis (PCA) to obtain suitable features. The genetic algorithm based kmeans clustering approach is used to classify the five kinds of leukocyte in the reduced dimensions. The experimental results show that even though only leukocyte nucleus features are used for classification in our method, we achieve a high and promised accurate recognition rate.

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