Cellular neural networks and computational intelligence in medical image processing

Aizenberg, Igor; Aizenberg, Naum N.; Hiltner, Jens; Moraga, Claudio; Bexten, Erdmuthe Meyer zu

doi:10.1016/s0262-8856(00)00066-4

Cited by 55 publications

(19 citation statements)

References 22 publications

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“…The contour of the white blood cells has to be perfectly preserved for further feature extraction for the detection of hematological disorders like leukemia. Finally, the original image inside white cell regions is restored [5][6][7][8][9][10][11][12][13].…”

Section: The Proposed Computing Algorithmmentioning

confidence: 99%

Automatic Segmentation of Leukocytes for the Detection of Leukemia Using a New Computing Algorithm

Kanakaraj¹,

Prakash²

2018

Int J Adv Tech

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Section: The Proposed Computing Algorithmmentioning

confidence: 99%

Automatic Segmentation of Leukocytes for the Detection of Leukemia Using a New Computing Algorithm

Kanakaraj¹,

Prakash²

2018

Int J Adv Tech

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“…In addition, some of those methods have been jointly used (e.g., neural networks 17,18 , genetic algorithm with multilevel thresholds 19 or some variant of support vector machines 20 ).…”

Section: Supervised Classificationmentioning

confidence: 99%

Classifying image analysis techniques from their output

Guada¹,

Gómez²,

Rodríguez³

et al. 2016

IJCIS

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In this paper we discuss some main image processing techniques in order to propose a classification based upon the output these methods provide. Because despite a particular image analysis technique can be supervised or unsupervised, and can allow or not the existence of fuzzy information at some stage, each technique has been usually designed to focus on a specific objective, and their outputs are in fact different according to each objective. Thus, they are in fact different methods. But due to the essential relationship between them they are quite often confused. In particular, this paper pursues a clarification of the differences between image segmentation and edge detection, among other image processing techniques.

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“…The term cluster analysis (first used by Tryon, 1939) actually encompasses a number of different classification algorithms. In the context of electrophoresis gel image analysis, the samples can be clustered by ether statistical clustering algorithms or neural network approaches [9][10][11]. A general question facing researchers in many areas of inquiry is how to organize observed data into meaningful structures.…”

Section: Cluster Analysismentioning

confidence: 99%

Cluster analysis method in electrophoretic patterns classification

Spirovski

Стојаноски

Mitrevski

2005

JICS

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The conventional electrophoresis methods are well known techniques for protein detection and analysis of cerebrospinal fluid (CSF). Disc electrophoresis (DEP) was carried out for detection of oligoclonal IgG bands in cerebrospinal fluid (CSF) on polyacrilamide gel. However, the advance of automation has made rapid collection of large amounts of data feasible and the development of microcomputers has made sophisticated processing even of old electrophoregrams possible. Automated analysis, data storage and sophisticate data acquisition were carried out with Gel Pro Analyzer 3.1, which is specifically structured to analyze gels and elctrophoregrams: complex band pattern matching (gel variation, dendogram analysis etc.); lane relation studies (sophisticated lane database); general gel analysis (accurate molecular size, quantitative determination of protein mixture etc.). Clustering techniques have been applied for detection of intrathecal immune response. Different hierarchic cluster analysis methods such as single linkage, complete linkage, unweighted pair-group average (UPMGA) were used. In addition, other cluster characteristics such, distance matrix and Euclidean distance matrix were calculated. Pairing of electrophoresis methods and cluster image analysis, could lead to additional diagnostic information of inflammatory conditions of the central nervous system (CNS) or dysfunction of blood-CSF barrier.

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Cellular neural networks and computational intelligence in medical image processing

Cited by 55 publications

References 22 publications

Automatic Segmentation of Leukocytes for the Detection of Leukemia Using a New Computing Algorithm

Automatic Segmentation of Leukocytes for the Detection of Leukemia Using a New Computing Algorithm

Classifying image analysis techniques from their output

Cluster analysis method in electrophoretic patterns classification

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