Intelligent Content-Based Dermoscopic Image Retrieval with Relevance Feedback for Computer-Aided Melanoma Diagnosis

2017 ◽  
Vol 10 (1) ◽  
pp. 85-108 ◽  
Author(s):  
Khadidja Belattar ◽  
Sihem Mostefai ◽  
Amer Draa
Keyword(s):  
Skin Lesion ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Query Image ◽  
Melanoma Diagnosis ◽  
Computer Aided ◽  
Histogram Intersection ◽  
Dermoscopic Image ◽  
Image Query ◽  
Aided Diagnosis

The use of Computer-Aided Diagnosis in dermatology raises the necessity of integrating Content-Based Image Retrieval (CBIR) technologies. The latter could be helpful to untrained users as a decision support system for skin lesion diagnosis. However, classical CBIR systems perform poorly due to semantic gap. To alleviate this problem, we propose in this paper an intelligent Content-Based Dermoscopic Image Retrieval (CBDIR) system with Relevance Feedback (RF) for melanoma diagnosis that exhibits: efficient and accurate image retrieval as well as visual features extraction that is independent of any specific diagnostic method. After submitting a query image, the proposed system uses linear kernel-based active SVM, combined with histogram intersection-based similarity measure to retrieve the K most similar skin lesion images. The dominant (melanoma, benign) class in this set will be identified as the image query diagnosis. Extensive experiments conducted on our system using a 1097 image database show that the proposed scheme is more effective than CBDIR without the assistance of RF.

Mosaic-Based Relevance Feedback for Image Retrieval

2011 ◽  
pp. 837-841
Author(s):  
Odej Kao ◽  
Ingo la Tendresse
Keyword(s):  
Image Retrieval ◽  
Relevance Feedback ◽  
A Priori ◽  
Standard Approach ◽  
Content Based Image Retrieval ◽  
Query Image ◽  
Starting Point ◽  
Image Ranking ◽  
Image Query ◽  
Similar Sample

A standard approach for content-based image retrieval (CBIR) is based on the extraction and comparison of features usually related to dominant colours, shapes, textures and layout (Del Bimbo, 1999). These features are a-priori defined and extracted, when the image is inserted into the database. At query time the user submits a similar sample image (query-by-sample-image) or draws a sketch (query-by-sketch) of the sought archived image. The similarity degree of the current query image and the target images is determined by calculation of a multidimensional distance between the corresponding features. The computed similarity values allow the creation of an image ranking, where the first k, usually k=32 or k=64, images are considered retrieval hits. These are chained in a list called ranking and then presented to the user. Each of these images can be used as a starting point for a refined search in order to improve the obtained results.

scholarly journals CONTENT BASED IMAGE RETRIEVAL ASSOCIATE WITH THE INTERSECTION OF MULTI KERNAL RELEVANCE VECTOR MACHINE AND HISTOGRAM JOIN

2015 ◽  
pp. 238-241
Author(s):  
K. VAISHNAVI ◽  
G.P.RAMESH KUMAR
Keyword(s):  
Active Learning ◽  
Image Retrieval ◽  
Relevance Feedback ◽  
Nearest Neighbor ◽  
Relevance Vector Machine ◽  
Content Based Image Retrieval ◽  
Training Set ◽  
Query Image ◽  
Histogram Intersection ◽  
Polynomial Kernels

Relevance Feedback is an important tool for grasping user's need in Interactive Content Based Image Retrieval (CBIR). Keeping this in mind, we have build up a framework using Relevance Vector Machine Classifier in interactive framework where user labels images as appropriate and inappropriate. The refinement of the images shown to the user is done using a few rounds of relevance feedback. This appropriate and inappropriate set then provides the training set for the RVM for each of these rounds. The method uses Histogram Intersection kernel with this interactive RVM (IKRVM). It has a retrieval component on top of this which searches for those images for retrieving which falls in the nearest neighbor set of the query image on the basis of histogram intersection based identical ranking (HIIR). The experimental results shows that the proposed framework shows better precision when compared with Active learning based RVMActive implemented with Radial Basis or Polynomial Kernels.

Relevance Feedback as New Tool for Computer-Aided Diagnosis in Image Databases

2012 ◽  
pp. 86-106
Author(s):  
Issam El Naqa ◽  
Jung Hun Oh ◽  
Yongyi Yang
Keyword(s):  
Relevance Feedback ◽  
Clinical Decision Making ◽  
Clinical Decision ◽  
Computer Aided Diagnosis ◽  
Image Features ◽  
Query Process ◽  
Large Databases ◽  
Computer Aided ◽  
High Level ◽  
Aided Diagnosis

With the ever-growing volume of images used in medicine, the capability to retrieve relevant images from large databases is becoming increasingly important. Despite the recent progress made in the field, its applications in Computer-Aided Diagnosis (CAD) thus far have been limited by the ability to determine the intrinsic mapping between high-level user perception and the underlying low-level image features. Relevance Feedback (RFB) is a post-query process to refine the search by using positive and/or negative indications from the user about the relevance of retrieved images, which has been applied successfully in traditional text-retrieval systems for improving the results of a retrieval strategy. In this chapter, the authors review some recent advances in RFB technology, and discuss its expanding role in content-based image retrieval from medical archives. They provide working examples, based on their experience, for developing machine-learning methods for RFB in mammography and highlight the potential opportunities in this field for CAD applications and clinical decision-making.

Computer-aided diagnosis of mammographic masses using local geometric constraint image retrieval

Optik ◽  
2018 ◽  
Vol 171 ◽  
pp. 754-767 ◽  
Author(s):  
Qingliang Li ◽  
Richeng Xu ◽  
Haoyu Zhao ◽  
Lili Xu ◽  
Xiaoning Shan ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Computer Aided Diagnosis ◽  
Geometric Constraint ◽  
Computer Aided ◽  
Aided Diagnosis

Generic integration of content-based image retrieval in computer-aided diagnosis

2012 ◽  
Vol 108 (2) ◽  
pp. 589-599 ◽  
Author(s):  
Petra Welter ◽  
Benedikt Fischer ◽  
Rolf W. Günther ◽  
Thomas M. Deserno (né Lehmann)
Keyword(s):  
Image Retrieval ◽  
Computer Aided Diagnosis ◽  
Content Based Image Retrieval ◽  
Computer Aided ◽  
Aided Diagnosis

Computer-aided diagnosis of mammographic masses using geometric verification-based image retrieval

2017 ◽  
Author(s):  
Qingliang Li ◽  
Weili Shi ◽  
Huamin Yang ◽  
Huimao Zhang ◽  
Guoxin Li ◽  
...  
Keyword(s):  
Image Retrieval ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Aided Diagnosis ◽  
Geometric Verification

scholarly journals Interactive Content Based Image Retrieval using Multiuser Feedback

2017 ◽  
Vol 1 (4) ◽  
pp. 165
Author(s):  
M. Premkumar ◽  
R. Sowmya
Keyword(s):  
Image Retrieval ◽  
Association Rules ◽  
Relevance Feedback ◽  
Association Rule ◽  
Association Rule Mining ◽  
Human Perception ◽  
Content Based Image Retrieval ◽  
Rule Mining ◽  
Query Image ◽  
Feedback Techniques

Retrieving images from large databases becomes a difficult task. Content based image retrieval (CBIR) deals with retrieval of images based on their similarities in content (features) between the query image and the target image. But the similarities do not vary equally in all directions of feature space. Further the CBIR efforts have relatively ignored the two distinct characteristics of the CBIR systems: 1) The gap between high level concepts and low level features; 2) Subjectivity of human perception of visual content. Hence an interactive technique called the relevance feedback technique was used. These techniques used user’s feedback about the retrieved images to reformulate the query which retrieves more relevant images during next iterations. But those relevance feedback techniques are called hard relevance feedback techniques as they use only two level user annotation. It was very difficult for the user to give feedback for the retrieved images whether they are relevant to the query image or not. To better capture user’s intention soft relevance feedback technique is proposed. This technique uses multilevel user annotation. But it makes use of only single user feedback. Hence Soft association rule mining technique is also proposed to infer image relevance from the collective feedback. Feedbacks from multiple users are used to retrieve more relevant images improving the performance of the system. Here soft relevance feedback and association rule mining techniques are combined. During first iteration prior association rules about the given query image are retrieved to find out the relevant images and during next iteration the feedbacks are inserted into the database and relevance feedback techniques are activated to retrieve more relevant images. The number of association rules is kept minimum based on redundancy detection.

Directional Vector-Based Skin Lesion Segmentation — A Novel Approach to Skin Segmentation

2020 ◽  
Vol 20 (03) ◽  
pp. 2050021
Author(s):  
P. Nikesh ◽  
G. Raju
Keyword(s):  
Skin Lesion ◽  
Automatic Segmentation ◽  
High Accuracy ◽  
Lesion Segmentation ◽  
Seed Point ◽  
Skin Segmentation ◽  
Novel Approach ◽  
Segmentation Algorithms ◽  
Computer Aided ◽  
Aided Diagnosis

Efficient skin lesion segmentation algorithms are required for computer aided diagnosis of skin cancer. Several algorithms were proposed for skin lesion segmentation. The existing algorithms are short of achieving ideal performance. In this paper, a novel semi-automatic segmentation algorithm is proposed. The fare concept of the proposed is 8-directional search based on threshold for lesion pixel, starting from a user provided seed point. The proposed approach is tested on 200 images from PH2 and 900 images from ISBI 2016 datasets. In comparison to a chosen set of algorithms, the proposed approach gives high accuracy and specificity values. A significant advantage of the proposed method is the ability to deal with discontinuities in the lesion.

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