Absolute Mean Brightness Error Research Articles

De-occlusion and recognition of frontal face images: a comparative study of multiple imputation methods

AbstractIncreasingly, automatic face recognition algorithms have become necessary with the development and extensive use of face recognition technology, particularly in the era of machine learning and artificial intelligence. However, the presence of unconstrained environmental conditions degrades the quality of acquired face images and may deteriorate the performance of many classical face recognition algorithms. Due to this backdrop, many researchers have given considerable attention to image restoration and enhancement mechanisms, but with minimal focus on occlusion-related and multiple-constrained problems. Although occlusion robust face recognition modules, via sparse representation have been explored, they require a large number of features to achieve correct computations and to maximize robustness to occlusions. Therefore, such an approach may become deficient in the presence of random occlusions of relatively moderate magnitude. This study assesses the robustness of Principal Component Analysis and Singular Value Decomposition using Discrete Wavelet Transformation for preprocessing and city block distance for classification (DWT-PCA/SVD-L1) face recognition module to image degradations due to random occlusions of varying magnitudes (10% and 20%) in test images acquired with varying expressions. Numerical evaluation of the performance of the DWT-PCA/SVD-L1 face recognition module showed that the use of the de-occluded faces for recognition enhanced significantly the performance of the study recognition module at each level (10% and 20%) of occlusion. The algorithm attained the highest recognition rate of 85.94% and 78.65% at 10% and 20% occlusions respectively, when the MICE de-occluded face images were used for recognition. With the exception of Entropy where MICE de-occluded face images attained the highest average value, the MICE and RegEM result in images of similar quality as measured by their Absolute mean brightness error (AMBE) and peak signal to noise ratio (PSNR). The study therefore recommends MICE as a suitable imputation mechanism for de-occlusion of face images acquired under varying expressions.

A proposed CLCOA Technique Based on CLAHE using Cat Optimized Algorithm for Plants Images Enhancement

Image Enhancement is one of the mainly significant with complex techniques in image study. The purpose of image enhancement is to advance the optical presence of an image, or to support a “improved convert representation for future mechanized image processing. Various images similar medical images, satellite images, natural with even real life photographs which have a lowly contrast and noise. This study presents a new enhancement technique based on standard contrast limited adaptive histogram equalization (CLAHE) technique for image enhancement which its name CLCOA. The suggested technique depends on augmentation of swarm intelligence via using Cat Swarm Optimization algorithm (CSO). The swarm intelligence is used to obtain the optimal structure of CLAHE technique. Tomato plant images have used and applied as dataset because of its important and influence in our life. For fair analysis of two techniques, Absolute Mean Brightness Error (AMBE), peak signal-to-noise ratio (PSNR), entropy and Contrast Gain of fundus images are analyzed by using MATLAB. The results show that performance of the proposed technique reveals the efficiently and robustness when compared results of standard technique.

Color image enhancement technique based on interval-valued intuitionistic fuzzy set

In this paper, a new interval-valued intuitionistic fuzzy-based algorithm is proposed to enhance low-light images. In this proposed algorithm, the low-light image is first changed to the fuzzy image by normal fuzzification. Then the above is converted to an intuitionistic fuzzy image and further to the interval-valued intuitionistic fuzzy image. After applying contrast limited adaptive histogram equalization, we derive the required image. In the experimental part, the proposed algorithm is compared with other existing algorithms including histogram equalization, contrast limited adaptive histogram equalization, histogram specification, dehazing, fractional order image enhancement, intuitionistic fuzzy, and interval-valued intuitionistic fuzzy. In terms of overall visual quality and performance metrics, the results showed that the proposed technique surpasses other current methods including entropy, absolute mean brightness error, and contrast improvement index.

Enhancing Detection of Microcalcifications using FADHECAL for Early Stage Breast Cancer

Microcalcifications (MCCs) are reliable early signs of breast cancer. However, the small size of calcifications and low radiation factors used in digital mammograms cause low and poor quality mammogram images in detecting MCCs. This paper presents an image enhancement technique called Fuzzy Anisotropic Diffusion Histogram Equalization Contrast Adaptive Limited (FADHECAL) to enhance the details of MCCs in mammogram images by reducing the image noise while conserving contrast and brightness. A total of 23 mammogram images with MCCs were retrieved from the Mammographic Image Analysis Society’s database. The enhancement performance of FADHECAL was compared with Recursive Mean-Separate Histogram Equalization, Histogram Equalization and Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization. Image quality measurement tools of absolute mean brightness error (AMBE), structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) were used. The results showed that FADHECAL had the most superior results among other enhancement techniques, with 6.302 of AMBE, 20.453 of PSNR and 0.851 of SSIM. The proposed FADHECAL exhibited a high accuracy of 91.30% for the detection of MCCs. Hence, FADHECAL can be used as an ideal tool for identifying MCCs in early-stage breast cancer.

SLAAHE: Selective Apex Adaptive Histogram Equalization

Medical imaging enables specialists to make diagnoses our internal body organs for detecting anomalies without being intrusive. Unfortunately, the images produced by various modalities are not only plagued by noises, but the images contrasts are also subpar, making it impossible to use them for a delicate task like diagnosis, which is directly concerned with our health. Pre-processing the image becomes obligatory to ameliorate its medical worth and contrast enhancement is a sacrosanct part of diagnostic image pre-processing. Even though some popular contrast enhancement algorithms, such as Adaptive Histogram Equalization (AHE), Contrast Limited Adaptive Histogram Equalization (CLAHE), and Minimum Mean Brightness Error Bi-Histogram Equalization (MMBEBHE), etc., are widely used, they are insufficient for some medical imaging modalities, such as ultrasound imaging (USG). This paper, named as Selective Apex Adaptive Histogram Equalization (SLAAHE), proposes an effective and efficient contrast enhancement algorithm that dynamically selects the best window size for dividing the low contrast image into image grids and clip limit for clipping the histogram of the low contrast sub-image only. Unique feature of the proposed algorithm is that it clips the image histogram only when it is not well-distributed and ennobles the output image contrast. This makes the algorithm best concerning the contrast enhancement quality, and reduced time and space complexity. Both theoretical analysis and assessment of experimental results with statistical metrics like Universal Image Quality Index (UIQI), Absolute Mean Brightness Error (AMBE), Multi-Scale Structural Similarity Index Measure (MSSSIM), Entropy, and Root Mean Square Error (RMSE) exhibit how the proposed method has gained momentous improvement over existing state-of-the-art methods. For the evaluation metrics UIQI, AMBE, MSSSIM, Entropy, and RMSE, the suggested technique received average scores of 0.79, 0.12, 0.88, 0.92, and 11.52 respectively.

Iterative histogram equalization using discrete wavelet transform in low-dynamic range

A progressive histogram equalization (HE) and contrast enhancement method is proposed in the frequency domain. This method has an iterative structure based on the 6-sigma rule in the low-dynamic range using discrete wavelet transform. The proposed method determines an automatic threshold for attenuating the high-frequency amplitude of a signal in the discrete wavelet domain, based on the standard deviation of the absolute power of the high-frequency components in the signal band generated from the probability density function (PDF). Then an iterative quantization procedure is used where the PDF values in the frequency domain are randomly quantized. Besides, the maximum Bhattacharyya coefficient, which matches the original input image’s PDF, is used as a cost function to optimize the histogram smoothing output. For the random number generation, a 32-bit pseudorandom generator is employed to produce the same result for the output image at each runtime. The quantization factor parameter enables a controlled contrast enhancement rate to receive balanced equalization results in images. In the experimental studies, the proposed iterative HE method is compared with the well-known global, local, and brightness preserving algorithms. Experimental studies quantitatively and qualitatively display promising and encouraging results in terms of various state-of-the-art quality assessment metrics such as mean squared error, peak signal-to-noise ratio, structural similarity index measurement, contrast improvement index, absolute mean brightness error, and quality-aware relative contrast measure.

Structure-preserving Contrast Enhancement of Fundus Images using Dualistic Sub-Image Bi-histogram Bezier Curve

Diabetic retinopathy is a common eye disease among diabetic patients which is caused by excessive sugar in the blood vessels that damage the retina. Fundus images are retina images that are captured and diagnosed by ophthalmologists. Ophthalmologists diagnose the progressive stages of diabetic retinopathy so that early detection of pre-diabetic retinopathy can be carried out. However, the quality of the fundus image can be associated with the brightness of the background and the indistinctive vessel contrast. This paper presents a novel extension of Bi-histogram Bezier curve contrast enhancement (BBCCE) based on the mean partition of its histogram. The disadvantage of having mean as the threshold partition is that the histogram distribution can be skewed due to an outlier. The proposed Dualistic Sub-Image Bi-histogram Bezier Curve Contrast Enhancement (DSI-BBCCE) method partitions the original histogram into two, using the median of the active dynamic intensity range of the input image and process two Bezier transform curves separately to replace the original cumulative density function curve as the median is not affected by the outlier. This DSI-BBCCE has the advantage of preserving the structure, median brightness and preventing over enhancement. The result shows that DSI-BBCCE has achieved a power signal to noise ratio (PSNR) of 20.08±0.94 dB, absolute mean brightness error (AMBE) of 20.15±1.89, structural similarity index model (SSIM) of 0.8096±0.0185, structure measure operator (SMO) of 3.2±1.10 and lightness measure order (LMO) of 200.90±44.19.

Optimized S-Curve Transformation and Wavelets-Based Fusion for Contrast Elevation of Breast Tomograms and Mammograms.

For the purpose of accuracy in detection and diagnosis, Computer-Aided Diagnosis (CAD) is preferred by radiologists for the analysis of Breast Cancer. However, the presence of noise, artifacts, and poor contrast in breast images during acquisition highlights the need for sophisticated enhancement techniques for the proper visualization of region-of-interest (ROI). In this work, contrast elevation of breast mammographic and tomographic images is performed with an improved S-Curve transform using the Particle Swarm Optimization (PSO) algorithm. The enhanced images are assessed using dedicated quality metrics such as the Enhancement Measure (EME) and Absolute Mean Brightness Error (AMBE) measurement. Although the enhancement techniques help in attaining better images, certain features relevant for diagnosis purposes are removed during the enhancement process, creating contradictions for radiological interpretation. Hence, to ensure the retention of diagnostic features from original breast tomograms and mammograms, a Discrete Wavelet Transform (DWT)-based fusion approach is incorporated, which fuses the original and contrast-enhanced images (with optimized s-curve transformation function) using the maximum fusion rule. The fusion performance is thereafter measured using the Image Quality Index (IQI), Standard Deviation (SD), and Entropy (E) as fusion metrics.

Gamma Correction for Brightness Preservation in Natural Images

Due to improper acquisition settings and other noise artifacts, the image degraded to yield poor mean preservation in brightness. The simplest way to improve the preservation is the implementation of histogram equalization. Because of over-enhancement, it failed to preserve the mean brightness and produce the poor quality of the image. This paper proposes a multi-scale decomposition for brightness preservation using gamma correction. After transformation to hue, saturation and intensity (HSI) channel, the 2D- discrete wavelet transform decomposed the intensity component into low and high-pass coefficients. At the next phase, gamma correction is used by auto-tuning the scale value. The scale is the modified constant value used in the logarithmic function. Further, the scale value is optimized to obtain better visual quality in the image. The optimized value is the weighted distribution of standard deviation-mean of low pass coefficients. Finally, the experimental result is estimated in terms of quality assessment measures used as absolute mean brightness error, the measure of information detail, signal to noise ratio and patch-based contrast quality in the image. By comparison, the proposed method proved to be suitably remarkable in retaining the mean brightness and better visual quality of the image.

Image sub-division and quadruple clipped adaptive histogram equalization (ISQCAHE) for low exposure image enhancement.

In this paper, a novel image sub-division and quadruple clipped adaptive histogram equalization (ISQCAHE) technique is proposed for the enhancement of low exposure images. The proposed method involves, computation of the histogram which includes a new approach of image sub-division, enhancement controlling mechanism, modification of probability density function (PDF) and histogram equalization (HE). The original histogram is segmented into sub-histograms based on exposure threshold and mean, to preserve the brightness and entropy. Then, individual sub-histogram is clipped separately to control the enhancement rate. For enhancing the visual quality, HE is applied to individual sub-histogram using the modified PDF. The experimental results show that, the proposed ISQCAHE method avoids the unpleasant artifacts effectively and provide a natural appearance to the enhanced image. It is simple, adaptive and performs superior than other techniques in terms of visual quality, absolute mean brightness error, entropy, Natural image quality evaluation, brightness preservation, structure similarity index measure and feature similarity index measure.

Tri-modal technique for medical images enhancement

<span>Owing to methods of acquisition, medical images often require enhancement for them to serve the intended purpose of computer-aided diagnosis. Most medical image enhancement techniques are application specific, leading to the introduction of different enhancement methods for different medical images. In addition, the execution time of most of the previous enhancement methods is longer than necessary. Hence, there is a need for a method that produces fast and satisfactory results when deployed for the enhancement of several medical images. This paper proposes a tri-modal technique, involving a hybrid combination of unsharp masking, logarithmic transformation, and histogram equalization approaches, for medical image enhancement. Three classes of medical images: X-ray, magnetic resonance, and computer tomographic images are used for the evaluations of the proposed tri-modal method, where absolute mean brightness error, peak signal-to-noise ratio, and entropy are utilized as performance metrics. Both qualitative and quantitative evaluations reveal that the proposed tri-modal method performed better than the four previous methods in the literature for the three classes of medical images used in the evaluation. Also, the execution time of the tri-modal technique compares well with those of mono-mode methods. Thus, the tri-modal technique produces better enhanced medical images from different medical image inputs.</span>

An Approach for Enhancement of MR Images of Brain Tumor

Magnetic Resonance Imaging plays an important role in diagnosing the brain tumor accurately, but it requires the approach to enhance the magnetic resonance images to assist physicians in brain tumor detection and making the treatment plan precisely to reduce the mortality rate. Therefore, in this proposed work, a comprehensive learning-based elephant herding optimization technique has been introduced to select the optimal value of smoothness factor in Bi-Histogram Equalization with Adaptive Sigmoid Function that enhances the visual quality as well as the appearance of the suspicious regions in magnetic resonance images. Further, the enhancement performance has been evaluated by the enhancement quality metrics. The metrics used include mean square error, peak signal to noise ratio, mean absolute error, structural similarity index metric, feature similarity index metric, Riesz transformed based feature similarity index metric, spectral residual-based similarity index metric, and absolute mean brightness error. The outcomes of this proposed work have a remarkable impact on enhancing magnetic resonance images and providing visual assistance for diagnosing brain tumors. The performance of the evaluation metrics is verified with Friedman's mean rank test, which strongly indicates a statistical difference between the proposed method and state-of-the-art methods.

A hybrid approach for enhancement of abdominal CT images

In this technological era, digital imaging is integrated into every aspect of our life, and it has a predominant and essential role in medical investigations. Different types of diseases are being diagnosed using Computed Tomography (CT) images, acquired with varying bit depths, and kept in conventional Digital Imaging and Communications in Medicine (DICOM) format. However, one of the key challenges faced by the practitioners experimenting in the field of biomedical image analysis is that the images acquired are of low contrast. This research work aims to propose a method centered on the windowing technique and anisotropic diffusion filter to enhance the contrast of medical images for further analysis. The suggested approach does adjustments of contrast and brightness using the information extracted from the DICOM header of considered abdominal CT images to efficiently visualize the details for further processing. Furthermore, we have analyzed and compared its performance with distinct notable techniques which have been typically used for the enhancement of the medical image. The recorded outcomes revealed that the suggested algorithm gives better results with average values of Peak Signal to Noise Ratio (PSNR), Absolute Mean Brightness Error (AMBE), and Feature Similarity (FSIM) index equivalent to 27.71 dB, 8.38, and 0.96, respectively. Further, the average value of the Universal Image Quality (UIQ) index and Edge Content (EC) is equivalent to 0.83 and 9.37, respectively. Based on the experimental results performed on 15,000 images of a real human CT dataset, we have observed that the suggested algorithm gives much better results from subjective and objective perspectives.

EFFICIENT RETINAL IMAGE ENHANCEMENT USING MORPHOLOGICAL OPERATIONS

Manual analysis of retinal images is a complicated and time-consuming task for ophthalmologists. Retinal images are susceptible to non-uniform illumination, poor contrast, transmission error, and noise problems. For the detection of retinal abnormalities, an efficient technique is required that can identify the presence of retinal complications. This paper proposes a methodology to enhance retinal images that use morphological operations to improve the contrast and bring out the fine details in the suspicious region. The enhancement plays a vital role in detecting abnormalities in the retinal images. Luminance gain metric ([Formula: see text] is obtained from Gamma correction on luminous channel of [Formula: see text]*[Formula: see text]*[Formula: see text] (hue, saturation, and value) color model of retinal image to improve luminosity. The efficiency and strength of the proposed methodology are evaluated using the performance evaluation parameters peak signal to noise ratio (PSNR), mean square error (MSE), mean absolute error (MAE), feature structural similarity index metric (FSIM), structural similarity index metric (SSIM), spectral residual index metric (SRSIM), Reyligh feature similarity index metric (RFSIM), absolute mean brightness error (AMBE), root mean square error (RMSE), image quality index (IQI), and visual similarity index (VSI). It has been revealed from the results and statistical analysis using the Friedman test that the proposed method outperforms existing state-of-the-art enhancement techniques.

Developing a Gaussian Model-based Histogram Equalization Technique for Enhancement of Breast Thermography Images

Purpose: Breast cancer is one of the most prevalent diseases among women worldwide. One of the effective ways to reduce the risk of death from breast cancer is early detection by breast screening methods such as thermography. Thermography is non-invasive infrared imaging that detects early symptoms of breast angiogenesis based on the temperature difference and asymmetric patterns between left and right breasts. For better visual perception, it is essential to increase the medical image quality and contrast.
 Materials and Methods: Histogram Equalization (HE) is a common and effective technique for contrast enhancement that uses the whole dynamic range of gray levels. In this paper, we propose to apply the equalization technique to the object part of the image rather than the background. One way is to use Otsu's method for automatic image thresholding. A more efficient approach to extract the body region is to fit a bimodal Gaussian distribution on the temperature information and restrict the equalization on gray level ranges corresponding to temperatures between the mean minus/plus three times of standard deviation.
 Results: We compared the performance of the proposed approach with six conventional HE methods by using objective criteria, including Absolute Mean Brightness Error (AMBE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSI), and Entropy.
 Conclusion: Based on objective measures, as well as subjective visual inspection of the results, the proposed Gaussian model-based HE has better performance in contrast enhancement and brightness preservation among other methods.

Bi Histogram Equalization Based Image Enhancement with Bicubic Interpolation

In image processing, enhancement histogram equalization is the widely used technique for contrast enhancement. However, this technique tends to change the brightness of the image. Here, the contrast and resolution of image were enhanced using the proposed Bi Histogram Equalization Based Image Enhancement with Bicubic Interpolation (BHBI) technique. Bi Histogram for contrast enhancement and bicubic interpolation for resolution enhancement has taken. Bi Histogram Equalization separates the input image's histogram into two based on input mean before equalizing them independently. Bicubic Interpolation can generate bigger or high-resolution image from one or more low resolution or smaller images. The performance of the BHBI method can be compared for some typical image’s cameraman lens that are applied to some existing enhancement methods like Adaptive Gamma Correction with Weighting Distribution (AGCWD), Adaptive Scale Adjustment Design of Unsharp Masking Filters (ASAUMF), Averaging Histogram Equalization (AVGHEQ), and Median-Mean Based Sub-Image-Clipped Histogram Equalization (MMSICHE). The performance of these existing techniques can be evaluated subjectively in terms of person illustration observation and measurably using Discrete Entropy (DE), Image Quality Index (IQI), Normalized Correlation Coefficient (NCC), Contrast Improvement Index (CII), and Absolute Mean Brightness Error (AMBE). The results obtained from the BHBI technique shows better when compared with respect to the various existing techniques.

A Noise-robust and Overshoot-free Alternative to Unsharp Masking for Enhancing the Acuity of MR Images.

Poor acutance of images (unsharpness) is one of the major concerns in magnetic resonance imaging (MRI). MRI-based diagnosis and clinical interventions become difficult due to the vague textural information and weak morphological margins on images. A novel image sharpening algorithm named as maximum local variation-based unsharp masking (MLVUM) to address the issue of 'unsharpness' in MRI is proposed in this paper. In the MLVUM, the sharpened image is the algebraic sum of the input image and the product of the user-defined scale and the difference between the output of a newly designed nonlinear spatial filter named maximum local variation-controlled edge smoothing Gaussian filter (MLVESGF) and the input image, weighted by the normalised MLV. The MLVESGF is a locally adaptive 2D Gaussian edge smoothing kernel whose standard deviation is directly proportional to the local value of the normalized MLV. The values of the acutance-to-noise ratio (ANR) and absolute mean brightness error (AMBE) shown by the MLVUM on 100 MRI slices are 0.6463 ± 0.1852 and 0.3323 ± 0.2200, respectively. Compared to 17 state-of-the-art image sharpening algorithms, the MLVUM exhibited a higher ANR and lower AMBE. The MLVUM selectively enhances the sharpness of edges in the MR images without amplifying the background noise without altering the mean brightness level.

Comparative performance analysis of Fuzzy Logic and Particle Swarm Optimization (PSO) techniques for image quality improvement: With special emphasis to old and distorted folk paintings

Cultural artwork has always held an important place in the society. These artworks hold within themselves a treasure trove of information about one’s society/community, their way of living and their history and many more things. However such artworks are not properly maintained and need to be carefully preserved. Preserving them digitally can give them a long life and the digital image processing techniques can be used to restore or enhance the quality of degraded artworks. This paper presents a comparison of two such techniques, PSO (Particle Swarm Optimization) and Fuzzy logic technique for enhancement of the distorted images. Image parameters like PSNR (Peak-to-Signal Noise Ratio), SSIM (Structure Similarity Index), AMBE (Absolute Mean Brightness Error), r (Pearson Correlation Coefficient), NMSE (Normalized Mean Square Error), IEF (Image Enhancement Factor) and CPP (Contrast per Pixel) have been tabulated to compare the performance of both the techniques. MATLAB software has been used for implementation of the algorithms and calculations of the parameters.

Intelligent Image Enhancement System based on Similarity Pixels

The main goal of image enhancement is to enhance the fine details present in the images having low luminance for better image quality. In the digital image processing field, the enhancement and removing the noise from the image is a critical issue; image noise removal is the manipulation of the image data to produce a visually high-quality image. The important details and useful information on image decreasing by the noise where the noise treated as information. The filters are used to remove unwanted information. The filters’ objectives are to improve the image quality. This paper proposes an enhancement image system, which chooses the appropriate filter and value of center pixel depends on the number of similarities adjusted neighbors pixels to the center pixel. The performance of this system is evaluated by using different quality metrics, such as Mean square error (MSE), Peak Signal Noise to Ratio (PSNR), Absolute Mean Brightness Error (AMBE), Measure of Enhancement (EME), and Measure of Enhancement by Entropy (EMEE), Entropy, Second-Order Entropy (SOE), and Image Enhancement Metric (IEM). The proposed enhancement system is efficient in removing noises and enhancing the image quality. Experiments are applied to a set of images, such as Lena, butterfly, etc. with different image sizes. The results show that the enhancement quality was performed well in the proposed system with minimal unexpected artifacts as compared to the other techniques, where the results of the proposed system for MSE, PSNR, AMBE, Entropy, SOE, EME, EMEE, and IEM for baboon image with the size 255x 255 are 2.906, 8.875, 3.92, 5.154, 2.692, 3.915, 0.442 and 3.674 in sequence.

Contrast Enhancement of Scintigraphic Image Using Fuzzy Intensification.

The objective of this study was to see the effect of fuzzy intensification (INT) operator on enhancement of scintigraphic image. Nuclear medicine physician (NMP) provided 25 scintigraphic images that required enhancement. The image pixels value was converted into fuzzy plane and was subjected to contrast INT operator with parameters of INT operator i.e., cross-over = 0.5 and number of iterations = 1 and 2. The enhanced image was again brought back into spatial domain (de-fuzzification) whose intensity value was in the range 0-255. NMP compared the enhanced image with its input image and labeled it as acceptable or unacceptable. The quality of enhanced image was also accessed objectively using four different image metrics namely: Entropy, edge content, absolute mean brightness error and saturation metrics. Most of the enhanced images (18 out of 25 images) obtained at cross-over = 0.5 and number of iterations = 1 are acceptable and found to have overall better contrast compared to the corresponding input image. Four images (two brain positron emission tomography scan and two I-131 scan) obtained at cross-over = 0.5 and with iteration = 2 are acceptable. Three input images (one dimercaptosuccinic acid (DMSA), one I-131 and one I-131- metaiodo-benzyl-guanidine (MIBG) scan) were better than their enhanced images. The enhancement produced by fuzzy INT operator was encouraging. Majority of enhanced images were acceptable at cross-over = 0.5 and number iterations = 1.