Content-based medical image classification using a new hierarchical merging scheme

Pourghassem, Hossein; Ghassemian, Hassan

doi:10.1016/j.compmedimag.2008.07.006

Cited by 85 publications

(75 citation statements)

References 15 publications

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“…The relevant classes are class 05, class 07, class 08, class 09, class 11, class 19, class 21, and class 38, respectively. In Table 2, the scores of relevant classes are greater than the score threshold of 30 which these scores are obtained based on fusion of the dependency probabilities P m′,n (m′=5, 7,8,9,11,19,21,38 and n= 1,⋯,7) of 7 feature descriptors. The largest dependency probability is related to P 11,7 where is equal to 0.9781, and it means that conditional probability of query belonging to class 11 using the seventh feature descriptor (angular function FDs) is a b Figure 11 shows the retrieval results of 20 images for this query.…”

Section: Retrieval Resultsmentioning

confidence: 99%

“…Then, the IRP algorithm is used for the feature similarity ranking level fusion and ultimately k top images in the final ranking list are displayed to the user as the retrieval results. This process is Table 2 An example of classification process: the score threshold value has been set to 30 and so the search space has been reduced to eight classes (m′= 5,7,8,9,11,19,21,38) Fig. 12, only two irrelevant images have been retrieved in the first RF (ranks 11 and 14), and in the second iteration of RF, there is no any irrelevant images in the retrieval results (Fig.…”

Section: Retrieval Resultsmentioning

confidence: 99%

“…Hence, by filtering out the images of irrelevant classes and reducing the search space, the performance of CBIR systems is improved significantly [7][8][9][10]. Pourghassem and Ghassemian [7] proposed a hierarchical medical image classification method using a merging scheme and multilayer perceptron classifiers. In this system, semantic classes are created from overlapping classes in the database.…”

Section: Introductionmentioning

confidence: 99%

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Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

Behnam

Pourghassem

2014

J Digit Imaging

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In this paper, a new content-based medical image retrieval (CBMIR) framework using an effective classification method and a novel relevance feedback (RF) approach are proposed. For a large-scale database with diverse collection of different modalities, query image classification is inevitable due to firstly, reducing the computational complexity and secondly, increasing influence of data fusion by removing unimportant data and focus on the more valuable information. Hence, we find probability distribution of classes in the database using Gaussian mixture model (GMM) for each feature descriptor and then using the fusion of obtained scores from the dependency probabilities, the most relevant clusters are identified for a given query. Afterwards, visual similarity of query image and images in relevant clusters are calculated. This method is performed separately on all feature descriptors, and then the results are fused together using feature similarity ranking level fusion algorithm. In the RF level, we propose a new approach to find the optimal queries based on relevant images. The main idea is based on density function estimation of positive images and strategy of moving toward the aggregation of estimated density function. The proposed framework has been evaluated on ImageCLEF 2005 database consisting of 10,000 medical X-ray images of 57 semantic classes. The experimental results show that compared with the existing CBMIR systems, our framework obtains the acceptable performance both in the image classification and in the image retrieval by RF.

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Section: Retrieval Resultsmentioning

confidence: 99%

Section: Retrieval Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

Behnam

Pourghassem

2014

J Digit Imaging

Self Cite

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“…Yang et al proposed a method based on NN backpropagation model to distinguish young corn plants from weeds by using color feature of the image as inputs [7]. Pourghassem et al applied Multilayer Perceptron (MLP) for hierarchical medical image classification [8]. To handle the high dimensionality of image database, image classification systems usually rely on a pre-processing step to reduce the computational cost with increase in retrieval accuracy.…”

Section: Introductionmentioning

confidence: 99%

Novel CBIR system based on Ripplet Transform using interactive Neuro-Fuzzy technique

Chowdhury

Das

Kundu

2012

ELCVIA

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Content Based Image Retrieval (CBIR) system is an emerging research area in effective digital data management and retrieval paradigm. In this article, a novel CBIR system based on a new Multiscale Geometric Analysis (MGA)-tool, called Ripplet Transform Type-I (RT) is presented. To improve the retrieval result and to reduce the computational complexity, the proposed scheme utilizes a Neural Network (NN) based classifier for image pre-classification, similarity matching using Manhattan distance measure and relevance feedback mechanism (RFM) using fuzzy entropy based feature evaluation technique. Extensive experiments were carried out to evaluate the effectiveness of the proposed technique. The performance of the proposed CBIR system is evaluated using a 2 × 5-fold cross validation followed by a statistical analysis. The experimental results suggest that the proposed system based on RT, performs better than many existing CBIR schemes based on other transforms, and the difference is statistically significant.

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“…In general, the existing CBIR methods extract features either from a whole image [2,25,26,29] or from a segmented image for further processing [16]. Since there could be undefined or ambiguous regions in an image, a classifier cannot be accurate enough when extracting features from a whole image.…”

Section: Segmentation and Main Object Detectionmentioning

confidence: 99%

Content-Based Image Retrieval System for Real Images

Huang¹,

Chen²

EPiC Series in Computing

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With the rapid progress of network technologies and multimedia data, information retrieval techniques gradually become content-based, and not text-based yet. In this paper, we propose a content-based image retrieval system to query similar images in a real image database. First, we employ segmentation and main object detection to separate the main object from an image. Then, we extract MPEG-7 features from the object and select relevant features using the SAHS algorithm. Next, two approaches "one-againstall" and "one-against-one" are proposed to build the classifiers based on SVM. To further reduce indexing complexity, K-means clustering is used to generate MPEG-7 signatures. Thus, we combine the classes predicted by the classifiers and the results based on the MPEG-7 signatures, and find out the similar images to a query image. Finally, the experimental results show that our method is feasible in image searching from the real image database and more effective than the other methods.

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Content-based medical image classification using a new hierarchical merging scheme

Cited by 85 publications

References 15 publications

Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

Optimal Query-Based Relevance Feedback in Medical Image Retrieval Using Score Fusion-Based Classification

Novel CBIR system based on Ripplet Transform using interactive Neuro-Fuzzy technique

Content-Based Image Retrieval System for Real Images

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