Medical Evaluation of Marker Controlled Watershed Technique for Detecting Brain Tumors

Kumar, Navdeep; Arora, Sugandha

doi:10.5120/8609-2461

Cited by 2 publications

(1 citation statement)

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“…In this method, the foreground and background marks are marked by opening and closing by reconstruction method. Due to this, we got limited number of region [19]. Fig.6.…”

Section: Marker-controlled Watershed Segmentationmentioning

confidence: 99%

CAD system: a content based image retrieval approach for pulmonary nodule detection in CT images

Ram¹,

Shylaja²,

Sahayadhas³

2018

IJET

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This paper proposes Computerized Aided Detection System (CAD) which uses Content Based Image Retrieval (CBIR) to detect cancer nodules present in an image. The CAD system is concerned for the radiologists to identify lung cancer at premature stages, which are very tiny nodules that are not able to seen by naked eye. In modern years, Image processing techniques play a key role in predicting diseases at early stages in particular in various cancer types such as liver cancer, breast cancer etc. This paper comprises of four steps: i) preprocessing an image in order to lessen the noise level and the accuracy of the image is to be improved, so that the accuracy in detection will be higher. ii) The image is segmented based on Marker-Controlled Watershed Segmentation. iii) The features of the nodules present in the image are extracted using GLCM. iv) The nodules are classified based on the extracted features using KNN classifier. The Content Based Image Retrieval Technique is used which is used to redeem query based images in the database by combining feature extraction and similarity matching methods. For experimentation of proposed technique, CT images are used which are extracted from Lung Image Database Consortium database (LIDC).

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“…In this method, the foreground and background marks are marked by opening and closing by reconstruction method. Due to this, we got limited number of region [19]. Fig.6.…”

Section: Marker-controlled Watershed Segmentationmentioning

confidence: 99%