Image Enhancement Techniques: A Study

Janani, P; Premaladha, J.; Ravichandran, K. S.

doi:10.17485/ijst/2015/v8i22/79318

Cited by 51 publications

(29 citation statements)

References 13 publications

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“…MR images often suffer from different types of noises, artefacts and inhomogeneity due to diverse reasons which hinder the imaging procedure. The brightness of the image should be uniform while the film density which is changed to form an image designated as noise [8]. There are some common sources of noise listed below:…”

Section: Noises In Mrimentioning

confidence: 99%

Detection of Breast Cancer using MRI: A Pictorial Essay of the Image Processing Techniques

Jaglan¹,

Dass²,

Duhan³

2019

IJCERT

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Medical imaging generates the visual representation of the interior body parts for the clinical analysis/ medical intervention. Now a days, an advanced medical imaging technique i.e. MRI provides acute dissection anatomical information about the human soft tissues. MRI generally suffers from poor contrast, low quality due to improper brightness & blurriness. So contrast manipulation is compulsively needed. Image enhancement is taken as the initial step which defines the accuracy of result. The prime objective is to enhance the visual appearance for further image analysis i.e. detection, segmentation, feature extraction/selection and even classification. Out of all the current image enhancement methods, the appropriate choice must be influenced by the facts i.e. visual perspective, modality and climatic conditions. The noise model and the filter reconstruction mainly decide the trade-off between noise reduction and feature preservation of the original image. In this paper, Median filter (MF), Average filter (AF), Wiener Filter (WF) and Gaussian filter (GF) are used to compare the effects of most dominant noises in MR images by calculating the statistical parameters i.e. Mean Square Error, PSNR, RMSE and MAE. Also, the MR images impinge with the variable noise density for effective comparative analysis of the filters. Further, the proposed algorithm detected the tumor region appropriately.

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Section: Noises In Mrimentioning

confidence: 99%

Detection of Breast Cancer using MRI: A Pictorial Essay of the Image Processing Techniques

Jaglan¹,

Dass²,

Duhan³

2019

IJCERT

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“…To improve contrast and visibility, power law is again applied. Power law transform is represented by the equation [35]:…”

Section: Gamma Correction (Power Law) Againmentioning

confidence: 99%

Finger vein recognition using two parallel enhancement ppproachs based fuzzy histogram equalization

Jumaa¹,

Zidan²

2019

PEN

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This paper evaluates a set of enhancement stages for finger vein enhancement that not only has low computational complexity but also high distinguishing power. This proposed set of enhancement stages is centered around fuzzy histogram equalization. Two sets of evaluation are carried out: one with the proposed approach and one with another unique approach that was formulated by rearranging and cropping down the preprocessing steps of the original proposed approach. To extract features, a combination of Hierarchical Centroid and Histogram of Gradients was used. Both enhancement stages were evaluated with K Nearest Neighbor and Deep Neural Networks using 6 fold stratified cross validation. Results showed improvement as compared to three latest benchmarks in this field that used 6-fold validation.

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“…A histogram is a graph that shows frequency of occurring of pixels intensities in the whole image [8,9]. When a region is constituted only by noise, each pixel value has the same probability to occur.…”

Section: Histogram Equalizationmentioning

confidence: 99%

“…The forms regarding presented defects may be difficult to observe. In order to increases the capacities of the detection [7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Ultrasonic Image Enhancement to Internal Defect Detection during Material Inspection

Merazi-Meksen

Boudraa

2018

MATEC Web Conf.

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Ultrasonic waves are efficiently exploited to detect internal defects such as cracks and occlusions in non-destructive testing (NDT) of materials. In some automated procedures, emitter and receiver probes are displaced step by step and signals are displayed to form images that make diagnosis easier to interpret. However, too much images are unnecessarily stored when they correspond to safe zones (without presented defects). In this paper, we propose a method that enhances ultrasonic images quality in order to improve the discrimination between images containing or not a shape regarding a detected defect. The method is based on the adapted histogram equalization technique followed by morphological operations that erase all useless shapes on processed images.

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Image Enhancement Techniques: A Study

Cited by 51 publications

References 13 publications

Detection of Breast Cancer using MRI: A Pictorial Essay of the Image Processing Techniques

Detection of Breast Cancer using MRI: A Pictorial Essay of the Image Processing Techniques

Finger vein recognition using two parallel enhancement ppproachs based fuzzy histogram equalization

Ultrasonic Image Enhancement to Internal Defect Detection during Material Inspection

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