Implementation of hybrid image fusion technique for feature enhancement in medical diagnosis

In this paper, a texture feature extraction method using local energy and local correlation of Gabor transformed images is proposed and applied to an image retrieval system. The Gabor wavelet is known to be similar to the response of the human visual system. The outputs of the Gabor transformation are robust to variants of object size and illumination. Due to such advantages, it has been actively studied in various fields such as image retrieval, classification, analysis, etc. In this paper, in order to fully exploit the superior aspects of Gabor wavelet, local energy and local correlation features are extracted from Gabor transformed images and then applied to an image retrieval system. Some experiments are conducted to compare the performance of the proposed method with those of the conventional Gabor method and the popular rotation-invariant uniform local binary pattern (RULBP) method in terms of precision vs recall. The Mahalanobis distance is used to measure the similarity between a query image and a database (DB) image. Experimental results for Corel DB and VisTex DB show that the proposed method is superior to the conventional Gabor method. The proposed method also yields precision and recall 6.58% and 3.66% higher on average in Corel DB, respectively, and 4.87% and 3.37% higher on average in VisTex DB, respectively, than the popular RULBP method.

Section: Introductionmentioning

confidence: 99%

Content-based Image Retrieval Using Texture Features Extracted from Local Energy and Local Correlation of Gabor Transformed Images

Bu¹,

Kim²,

Lee³

et al. 2017

“…Therefore, it can be used to evaluate the performance of image processing systems, and thus help to select the optimal parameters in image processing. It is useful in many applications, such as image watermarking [2] and image enhancement [3]. To this end, various IQA methods have been proposed.…”

Section: Introductionmentioning

confidence: 99%

Blind Image Quality Assessment on Gaussian Blur Images

Wang¹,

Wang²,

Zhou³

2017

Multimedia is a ubiquitous and indispensable part of our daily life and learning such as audio, image, and video. Objective and subjective quality evaluations play an important role in various multimedia applications. Blind image quality assessment (BIQA) is used to indicate the perceptual quality of a distorted image, while its reference image is not considered and used. Blur is one of the common image distortions. In this paper, we propose a novel BIQA index for Gaussian blur distortion based on the fact that images with different blur degree will have different changes through the same blur. We describe this discrimination from three aspects: color, edge, and structure. For color, we adopt color histogram; for edge, we use edge intensity map, and saliency map is used as the weighting function to be consistent with human visual system (HVS); for structure, we use structure tensor and structural similarity (SSIM) index. Numerous experiments based on four benchmark databases show that our proposed index is highly consistent with the subjective quality assessment.

“…Some previous works presented analysis or processing methods [5][6][7][8] and transfer or exchange systems [9,10] for medical images. However, since they have focused on a specific type of HISs or clinical materials, they did not consider the diversity and semantic relations of medical data in multi-systems.…”

Section: Related Workmentioning

confidence: 99%

“…Nowadays most of medical institutions like hospitals naturally use HIS. Recently, many studies [1][2][3][4][5][6][7][8][9][10] have tried to efficiently store, transfer, and manage clinical materials. However, HIS consists of several systems such as Order Communication System (OCS), Electronic Medical Record (EMR), and Picture Archiving and Communication System (PACS) and they generate clinical materials of different types.…”

Section: Introductionmentioning

confidence: 99%

Image-Centric Integrated Data Model of Medical Information by Diseases: Two Case Studies for AMI and Ischemic Stroke

2016

In the medical fields, many efforts have been made to develop and improve Hospital Information System (HIS) including Electronic Medical Record (EMR), Order Communication System (OCS), and Picture Archiving and Communication System (PACS). However, materials generated and used in medical fields have various types and forms. The current HISs separately store and manage them by different systems, even though they relate to each other and contain redundant data. These systems are not helpful particularly in emergency where medical experts cannot check all of clinical materials in the golden time. Therefore, in this paper, we propose a process to build an integrated data model for medical information currently stored in various HISs. The proposed data model integrates vast information by focusing on medical images since they are most important materials for the diagnosis and treatment. Moreover, the model is disease-specific to consider that medical information and clinical materials including images are different by diseases. Two case studies show the feasibility and the usefulness of our proposed data model by building models about two diseases, acute myocardial infarction (AMI) and ischemic stroke.