Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications

Quintanilla-Domínguez, J.; Ojeda-Magaña, Benjamín; Cortina-Januchs, M. G.; Ruelas, R.; Vega-Corona, A.; Andina, Diego

doi:10.1016/j.scient.2011.04.009

Cited by 38 publications

(21 citation statements)

References 16 publications

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“…Recently, a lot of researchers use statistical clustering in image segmentation [3]. Contextual clustering [4,9] is a supervised algorithm for segmentation which uses spatial information by considering number of activated neighbors of voxel and following this a rule is designed for clustering.…”

Section: A Segmentation Via Contextual Clustering With Region Growingmentioning

confidence: 99%

Analysis Of Classification Methods For Diagnosis Of Pulmonary Nodules In CT Images

Baboo¹,

Iyyapparaj²

2017

IOSR JCE

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Section: A Segmentation Via Contextual Clustering With Region Growingmentioning

confidence: 99%

Analysis Of Classification Methods For Diagnosis Of Pulmonary Nodules In CT Images

Baboo¹,

Iyyapparaj²

2017

IOSR JCE

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“…Recently, a lot of researchers use statistical clustering in image segmentation [3] .In a clustering technique along with the region growing, each pixel is associated with one of the finite number of threshold is grown to form disjoint regions. The contextual clustering method proposed by [4,9] is a supervised algorithm.…”

Section: A Segmentation Via Contextual Clustering With Region Growingmentioning

confidence: 99%

Detection Of Classification Methods For Diagnosis Of Pulmonary Nodules In CT Images

Baboo¹,

Iyyapparaj²

2017

IOSR JCE

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“…based on the application, different methods have been used for image segmentation, such as edge detection, histogram threshold, ANN, stochastic models, region growing and clustering techniques [10]. In medical imaging, segmentation has a major role, since it helps in the delineation of anatomical structures.…”

Section: B Image Segmentationmentioning

confidence: 99%

“…A fuzzy set technique is used to determine the type of the mass diagnosed in the preview step. This system uses a rule base derived from knowledge of an expert, which is basically a set of statements or facts used for decision making [10].…”

Section: Introductionmentioning

confidence: 99%

Mass Detection in Lung CT Images using Region Growing Segmentation and Decision Making based on Fuzzy Systems

bagherieh¹,

Hashemi²,

Pilevar³

2013

IJIGSP

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-Lung cancer is distinguished by presenting one of the highest incidences and one of the highest rates of mortality among all other types of cancers. Detecting and curing the disease in the early stages provides the patients with a high chance of survival. In order to help specialists in the search and recognition of the lung nodules in tomography images, a good number of research centers have been developed in computer-aided detection (CAD) systems for automating the procedures. This work aims at detecting lung nodules automatically through computerized tomography images. Accordingly, this article aim at presenting a method to improve the efficiency of the lung cancer diagnosis system, through proposing a region growing segmentation method to segment CT scan lung images and, then, cancer recognition by FIS (Fuzzy Inference System).The proposed method consists of three steps. The first step was pre-processing for enhancing contrast, removing noise, and pictures less corrupted by LinearFiltering. In second step, the region growing segmentation method was used to segment the CT images. In third step, we have developed an expert system for decision making which differentiates between normal, benign, malignant or advanced abnormality findings. The FIS can be of great help in diagnosing any abnormality in the medical images. This step was done by extracting the features such as area and color (gray values) and given to the FIS as input. This system utilizes fuzzy membership functions which can be stated in the form of if-then rules for finding the type of the abnormality. Finally, the analysis step will be discussed and the accuracy of the method will be determined. Our experiments show that the average sensitivity of the proposed method is more than 95%.

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Image segmentation by fuzzy and possibilistic clustering algorithms for the identification of microcalcifications

Cited by 38 publications

References 16 publications

Analysis Of Classification Methods For Diagnosis Of Pulmonary Nodules In CT Images

Analysis Of Classification Methods For Diagnosis Of Pulmonary Nodules In CT Images

Detection Of Classification Methods For Diagnosis Of Pulmonary Nodules In CT Images

Mass Detection in Lung CT Images using Region Growing Segmentation and Decision Making based on Fuzzy Systems

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