Enhancement of dark images using dynamic stochastic resonance with anisotropic diffusion
01 Mar 2016-Journal of Electronic Imaging (International Society for Optics and Photonics)-Vol. 25, Iss: 2, pp 023017-023017
TL;DR: The method increases mean and variance of the image by the optimum iterations on low coefficients of images, which improves contrast and brightness, respectively, and simultaneously, edges also become sharper.
Abstract: Image enhancement techniques are intended to improve the quality of an image without any kind of distortion or degradation. The literature is rich enough in this area, but there also exist some limitations. A technique is proposed for image enhancement by combining anisotropic diffusion with dynamic stochastic resonance in discrete wavelet transform domain. The method increases mean and variance of the image by the optimum iterations on low coefficients of images, which improves contrast and brightness, respectively, and simultaneously, edges also become sharper. It is well demonstrated by performing on various test images. Specifically, the adaptation and efficiency of the proposed technique for medical images are shown, because generally medical images appear contaminated with noise in terms of low illumination.
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TL;DR: The proposed non-invasive CAD system based on brain Magnetic Resonance Imaging (MRIs) is capable of assisting radiologists and clinicians to detect not only the presence, but also the type of glioma tumors.
Abstract: The real time usage of Computer Aided Diagnosis (CAD) systems to detect brain tumors as proposed in the literature is yet to be explored. Gliomas are the most commonly found brain tumors in human. The proposed non-invasive CAD system based on brain Magnetic Resonance Imaging (MRIs) is capable of assisting radiologists and clinicians to detect not only the presence, but also the type of glioma tumors. The system is devised to work irrespective of the image pulse sequence. It uses different segmentation schemes for different pulse sequences, fusion of texture features, and ensemble classifier to perform three levels of classification. Once the tumor is detected at the first level of classification, its location is analyzed using tentorium of brain and it is classified into superatentorial or infratentorial in the next level. Based on the morphological and inherent characteristics of tumor (area, perimeter, solidity, and orientation), the system identifies tumor type at the third level of classification. The system reports average accuracy of 97.76% on JMCD (a dataset collected from local medical college) and 97.13% on BRATS datasets at the first level of classification. Average accuracy of 97.87% for astrocytomas, 94.24% for ependymoma, 96.29% for oligodendroglioma, and 98.69% for glioblastoma multiforme is observed for histologically classified JMCD dataset. The same is observed as 95.45% for low grade and 95.50% for high grade tumors in publically available BRATS dataset. The performance of the proposed CAD system is statistically examined through hypothetical Student’s t-test and Wilcoxon matched pair test. The performance of the system is also validated by domain experts for its possible real time usage.
57 citations
TL;DR: This work presents a non-invasive and adaptive method for detection of tumor from T2-weighted brain magnetic resonance images, enhanced by preprocessing and segmented through multilevel customization of Otsu’s thresholding technique.
Abstract: The detection of brain tumor is a challenging task for radiologists as brain is the most complicated and complex organ. This work presents a non-invasive and adaptive method for detection of tumor from T2-weighted brain magnetic resonance (MR) images. Non-homogeneous brain MR images are enhanced by preprocessing and segmented through multilevel customization of Otsu’s thresholding technique. Several textural and shape features are extracted from the segmented image and two prominent ones are selected through entropy measure. Support vector machine (SVM) classifies MR images using prominent features. Experiments are performed on a dataset collected from MP MRI & CT Scan Centre at NSCB Medical College Jabalpur and the other from Charak Diagnostic & Research Centre Jabalpur. More than 98% accuracy is reported with 100% sensitivity for both the datasets at 99% confidence interval. The proposed system is compared with several existing methods to showcase its efficacy.
43 citations
TL;DR: The proposed clinical decision support system utilizes fusion of MRI pulse sequences as each of them gives salient information for tumor identification and successfully identifies and classify tumor with Naive Bayes classifier.
Abstract: Brain tumor detection and identification of its severity is a challenging task for radiologists and clinicians. This work aims to develop a novel clinical decision support system to assist radiologists and clinicians efficiently in real-time. The proposed clinical decision support system utilizes fusion of MRI pulse sequences as each of them gives salient information for tumor identification. An adaptive thresholding is proposed for segmentation and centralized patterns are observed from LBP image of so obtained segmented image. Run length matrix extracted from these centralized patterns is used for tumor identification. The developed features successfully identify and classify tumor with Naive Bayes classifier. The proposed decision support system not only detects tumors, but also identifies its grading in terms of severity. As Glioma tumors are the most frequent among brain tumors, the proposed system is tested for the presence of low grade (Astrocytoma and Ependymoma) as well as high grade (Oligodendroglioma and Glioblastoma Multiforme) Glioma tumors on images collected from NSCB Medical College Jabalpur, India and BRATS dataset. The experiments performed on two datasets give more than 96% accuracy. The proposed decision support system is quite sensitive towards the detection and specification of tumors. All the results are verified by domain experts in real time.
40 citations
TL;DR: Simulation results show that the proposed adaptive bistable array stochastic resonance-based grayscale image restoration enhancement method significantly outperforms the classical image restoration methods both on thegrayscale level and the PSNR of the restored image, particularly in a low PSNR scenario.
Abstract: Considering the widespread noise interference in the two-dimensional (2D) image transmission processing, we proposed an optimal adaptive bistable array stochastic resonance (SR)-based grayscale image restoration enhancement method under low peak signal-to-noise ratio (PSNR) environments. In this method, the Hilbert scanning is adopted to reduce the dimension of the original grayscale image. The 2D image signal is converted into a one-dimensional (1D) binary pulse amplitude modulation (BPAM) signal. Meanwhile, we use the adaptive bistable array SR module to enhance the 1D low SNR BPAM signal. In order to obtain the restored image, we transform the enhanced BPAM signal into a 2D grayscale image signal. Simulation results show that the proposed method significantly outperforms the classical image restoration methods (i.e., mean filter, Wiener filter and median filter) both on the grayscale level and the PSNR of the restored image, particularly in a low PSNR scenario. Larger array size brings better image restoration effect.
21 citations
TL;DR: Results show that the proposed scheme augments colour results of greyscale-based contrast enhancement algorithms and is relatively less complex compared to most algorithms in the literature.
Abstract: This paper presents an effective colour enhancement framework for statistical and logarithmic image processing (LIP)-based enhancement algorithms. The proposed approach utilizes the fusion of partial, multiple computed luminance channels with colour image channel statistics obtained from the input colour image for adaptive colour enhancement. The proposed scheme does not modify the image intensity channel, avoiding colour fading typically observed in colour images processed with conventional algorithms. The colour enhancement scheme compensates for the weaknesses of greyscale-based contrast enhancement and illumination normalization algorithms by focusing on preserving/restoring or enhancing colour. The proposed system avoids the conversion to complex, non-linear colour spaces such as HSI and HSV while producing similar results without manual adjustment of parameters. Additionally, an adaptive scheme for detection of images with unbalanced colour and uneven illumination is combined with the proposed system. Results show that the proposed scheme augments colour results of greyscale-based contrast enhancement algorithms and is relatively less complex compared to most algorithms in the literature.
20 citations
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TL;DR: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced, chosen to vary spatially in such a way as to encourage intra Region smoothing rather than interregion smoothing.
Abstract: A new definition of scale-space is suggested, and a class of algorithms used to realize a diffusion process is introduced. The diffusion coefficient is chosen to vary spatially in such a way as to encourage intraregion smoothing rather than interregion smoothing. It is shown that the 'no new maxima should be generated at coarse scales' property of conventional scale space is preserved. As the region boundaries in the approach remain sharp, a high-quality edge detector which successfully exploits global information is obtained. Experimental results are shown on a number of images. Parallel hardware implementations are made feasible because the algorithm involves elementary, local operations replicated over the image. >
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TL;DR: In this paper, it was shown that a dynamical system subject to both periodic forcing and random perturbation may show a resonance (peak in the power spectrum) which is absent when either the forcing or the perturbations is absent.
Abstract: It is shown that a dynamical system subject to both periodic forcing and random perturbation may show a resonance (peak in the power spectrum) which is absent when either the forcing or the perturbation is absent.
2,774 citations
10 Dec 2002
TL;DR: It is shown that Peak Signal-to-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality and tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics.
Abstract: Human observers can easily assess the quality of a distorted image without examining the original image as a reference. By contrast, designing objective No-Reference (NR) quality measurement algorithms is a very difficult task. Currently, NR quality assessment is feasible only when prior knowledge about the types of image distortion is available. This research aims to develop NR quality measurement algorithms for JPEG compressed images. First, we established a JPEG image database and subjective experiments were conducted on the database. We show that Peak Signal-to-Noise Ratio (PSNR), which requires the reference images, is a poor indicator of subjective quality. Therefore, tuning an NR measurement model towards PSNR is not an appropriate approach in designing NR quality metrics. Furthermore, we propose a computational and memory efficient NR quality assessment model for JPEG images. Subjective test results are used to train the model, which achieves good quality prediction performance.
913 citations
TL;DR: This work develops the Retinex computation into a full scale automatic image enhancement algorithm—the multiscale RetineX with color restoration (MSRCR)—which com- bines color constancy with local contrast/lightness enhancement to transform digital images into renditions that approach the realism of direct scene observation.
Abstract: There has been a revivification of interest in the Retinex computation in the last six or seven years, especially in its use for image enhancement. In his last published concept (1986) for a Ret- inex computation, Land introduced a center/surround spatial form, which was inspired by the receptive field structures of neurophysi- ology. With this as our starting point, we develop the Retinex con- cept into a full scale automatic image enhancement algorithm—the multiscale Retinex with color restoration (MSRCR)—which com- bines color constancy with local contrast/lightness enhancement to transform digital images into renditions that approach the realism of direct scene observation. Recently, we have been exploring the fun- damental scientific questions raised by this form of image process- ing. 1. Is the linear representation of digital images adequate in visual terms in capturing the wide scene dynamic range? 2. Can visual quality measures using the MSRCR be developed? 3. Is there a canonical, i.e., statistically ideal, visual image? The answers to these questions can serve as the basis for automating visual as- sessment schemes, which, in turn, are a primitive first step in bring- ing visual intelligence to computers. © 2004 SPIE and IS&T.
598 citations
01 Sep 1996
TL;DR: A multi-scale retinex (MSR) which overcomes this limitation for most scenes and both color rendition and dynamic range compression are successfully accomplished except for some "pathological" scenes that have very strong spectral characteristics in a single band.
Abstract: The retinex is a human perception-based image processing algorithm which provides color constancy and dynamic range compression. We have previously reported on a single-scale retinex (SSR) and shown that it can either achieve color/lightness rendition or dynamic range compression, but not both simultaneously. We now present a multi-scale retinex (MSR) which overcomes this limitation for most scenes. Both color rendition and dynamic range compression are successfully accomplished except for some "pathological" scenes that have very strong spectral characteristics in a single band.
560 citations