Content Based Medical Image Retrieval Using Binary Association Rules

Akila, None

doi:10.3844/jcssp.2013.678.689

Cited by 5 publications

(4 citation statements)

References 14 publications

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“…The optimization method like genetic algorithm proposed in our work with respect to the medical images. The content based medical image retrieval framework proposed in (Maheswari, 2013), the brain image only considered. In the proposed work three different medical images like lungs, brain and liver are considered and the genetic based soft computing method based retrieval used.…”

Section: Discussionmentioning

confidence: 99%

Soft Computing Based Medical Image Retrieval Using Shape and Texture Features

Daisy¹

2014

American Journal of Applied Sciences

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Image retrieval is a challenging and important research applications like digital libraries and medical image databases. Content-based image retrieval is useful in retrieving images from database based on the feature vector generated with the help of the image features. In this study, we present image retrieval based on the genetic algorithm. The shape feature and morphological based texture features are extracted images in the database and query image. Then generating chromosome based on the distance value obtained by the difference feature vector of images in the data base and the query image. In the selected chromosome the genetic operators like cross over and mutation are applied. After that the best chromosome selected and displays the most similar images to the query image. The retrieval performance of the method shows better retrieval result.

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Section: Discussionmentioning

confidence: 99%

Soft Computing Based Medical Image Retrieval Using Shape and Texture Features

Daisy¹

2014

American Journal of Applied Sciences

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“…The obtained features were subjected to SVM classifier to decide if the tumor is available or not. Since the difference in the values of ART coefficients serves as a measure of closeness, the Akila and Uma maheswari [4] have developed a CBIR system applying binary association rules which involved a pre-processing stage for noise removal and features were extracted using moment invariant features which resulted in feature vectors. In the testing stage the query image feature vectors are classified by using Artificial Neural Network (ANN) as normal, benign, malignant.…”

Section: Literature Surveymentioning

confidence: 99%

An Intellectual Brain MRI Image Retrieval System to aid the Diagnosis of Brain Tumors

2019

IJRTE

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Human brain being a complex organ, detecting abnormalities like Brain Tumor, Alzheimer, and Schizophrenia etc. are not an easy task. A computer aided automated system called Content Based Medical Image Retrieval System (CBMIR) can be used to assist the medical practitioner in arriving at correct diagnosis. Brain tumor is a kind of disease related to the brain malfunction and goes through various stages. Brain tumor identification, classification in its initial stages is an important and challenging task. In this paper, focus is on revival of brain tumor images from large database where multiple stages of diseases are present. In order to realize this task, a feature extraction technique comprising of Local Binary Pattern (LBP), Gabor, and Histogram of Gradient (HOG) are used. Based on the attributes, Support Vector Machine (SVM) classifier is used for pattern learning and classification. An experiment is performed to measure the accuracy of SVM classifier and performance of the CBMIR system for different classes of brain tumor disease. With the extracted attributes, SVM achieves an accuracy of 89.33%, average precision of 89.35% and recall of 89.33%.

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“…Binary association rules are used to organize and mark significant features of image database. Then the similarity comparison is performed through trigonometric function distance similarity measurement algorithm (Akila and Maheswari, 2013). From this study, it is understood that this method works well on MRI images.…”

Section: Jcsmentioning

confidence: 99%

Image Retrieval Using Certain Block Based Difference of Inverse Probability and Certain Block Based Variation of Local Correlation Coefficients Integrated With Wavelet Moments

Thenkalvi¹

2014

Journal of Computer Science

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Content Based Image Retrieval (CBIR) is an evolving topic under image processing. Retrieval on medical images plays a vital role in saving mankind. Medical Content Based Image Retrieval (MCBIR) does not stop its work in displaying similar images; it also goes one step further for diagnosis to decide the method of therapy by comparing the query image and database image. Hence we believe that, our work is a mile stone in the medical imaging research and will gain an appreciable amount of demand in automatic diagnosis done through artificial intelligence. While handling a large amount of data base, retrieval naturally reduces the search area. Many research works were conducted on CBIR systems to yield better performance on retrieval of medical images. Here we propose a modified integrated approach which extracts low level image features: Color, intensity, shape and texture using Certain Block based Difference of Inverse Probability (CBDIP) and Certain Block based Variation of Local Correlation coefficients (CBVLC) for pre-processed images. Consequently wavelet moments are calculated on derived CBDIP and CBVLC values that leads to mining on medical images only with 48 feature vectors. Thereby greatly reducing total number of feature vectors used for similarity comparison for retrieval of similar images. To know the retrieval accuracy, precision and recall values are calculated for the mined 1000 images. It has been examined that this integrated approach shows an improvement in retrieval accuracy and the time taken for similarity comparison.

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Content Based Medical Image Retrieval Using Binary Association Rules

Cited by 5 publications

References 14 publications

Soft Computing Based Medical Image Retrieval Using Shape and Texture Features

Soft Computing Based Medical Image Retrieval Using Shape and Texture Features

An Intellectual Brain MRI Image Retrieval System to aid the Diagnosis of Brain Tumors

Image Retrieval Using Certain Block Based Difference of Inverse Probability and Certain Block Based Variation of Local Correlation Coefficients Integrated With Wavelet Moments

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