Multi-Regional Adaptive Image Compression (AIC) for Hip Fractures in Pelvis Radiography

Nasıfoğlu, Hüseyin; Eroğul, Osman; Ataç, Gökçe Kaan; Ozdemir, Galip

doi:10.1007/978-981-10-4166-2_10

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…The patterns of retina blood vessels of each person are unique and thus can be used for studying the identity of an individual. The video camera technique is used along with unobtrusive close infrared illumination method [5]. The determination of probability of each of the possible categories, the performance of classification is impossible to be judged [6].…”

Section: Introductionmentioning

confidence: 99%

Novel Technique for Iris Detection in Image Processing

.¹

2018

IJRET

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An automated method utilized for biometric identification which includes various mathematical pattern-recognition methods in it is known as the iris recognition method. The videos of the irises of various individual's eyes are studied in this technique. The complex random patterns present within this approach are single, constant moreover can as well viewed from a particular distance. In the base paper, Circular-Hough Transformation is applied with canny edge detection. The GLCM algorithm is applied which will extract the contrast, energy, entropy and heterogeneity of the detected iris has been calculated. To increase the accuracy of iris detection and reduce execution time, improvement in existing GLCM algorithm, feature extraction technique is being proposed. The proposed improvement will be based on applying structural tensor algorithm and improved GLCM for contrast detection.

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Section: Introductionmentioning

confidence: 99%

Novel Technique for Iris Detection in Image Processing

.¹

2018

IJRET

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Efficient ROI-based compression of mammography images

Abdellatif

Taha

El-Shanawany

et al. 2022

Biomedical Signal Processing and Control

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Medical Image Compression for Telemedicine Applications

Nasıfoğlu¹,

Eroğul²

2019

ABEB

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High resolution medical images obtained by different imaging modalities stored in PACS needs higher storage space and bandwidth because of requiring much space in memory. In this sense, compression of medical images is important for efficient use of database. The main purpose of image compression is to reduce the number of bits representing the image while preserving the image quality and the intensity level of the pixels as much as possible depending on grayscale or RGB image [1]. Since medical images also contain diagnostic information about a disease or an artifact, less or no loss of detail in terms of quality is desired while compressing the significant areas. Otherwise, there may be difficulties or misdiagnosis in the treatment planning. With the lossless image compression technique, it is possible to preserve the entire pixel data while reducing the image size. The disadvantage of this technique is that it does not gain high memory size due to the low compression performance. On the other hand, higher compression ratios can be obtained by compromising redundant data with the lossy compression technique. Loss of image quality seems to be a risky condition in terms of correct diagnosis, but it is possible to reach acceptable compression rates and control data loss by setting appropriate parameters without losing diagnostic information. Adaptive image compression (AIC) is a hybrid technique that combines both lossless and lossy image compression techniques [2,3]. When applying AIC, it is primarily necessary to detect regions of interest in order to determine which regions will be compressed as lossy or lossless. After determining the focused or noticeable regions of radiologist on graph, it is possible to sort and adaptively compress these regions by importance. If the first order region is considered as the area containing the most information for the diagnosis, lossless compression can be applied here so as to avoid loss of detail. If there are second, third, and continuing order regions of interest, it may be preferable to adaptively compress these areas with little loss. Non-ROI (Non-Region of Interest) parts can be considered as less important or healthy areas in the diagnostic sense. Therefore, higher compression ratios can be achieved by compromising more details for these regions. After applying AIC, reconstructed image should be evaluated as sufficient and acceptable by the physician in terms of diagnostic information.Thus, the compressed images are recommended to be evaluated with subjective criteria in addition to objective criteria.The most common used objective criteria parameters for evaluating the compression performance are Compression Ratio (CR), Bits per Pixel (BPP), Mean Square Error (MSE) and the Peak Signal to Noise Ratio (PSNR). Compression ratio is defined as the

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Multi-Regional Adaptive Image Compression (AIC) for Hip Fractures in Pelvis Radiography

Cited by 3 publications

References 9 publications

Novel Technique for Iris Detection in Image Processing

Novel Technique for Iris Detection in Image Processing

Efficient ROI-based compression of mammography images

Medical Image Compression for Telemedicine Applications

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