Optic disc (OD) detection is an important step in developing systems for automated diagnosis of various serious ophthalmic pathologies. This paper presents a new template-based methodology for segmenting the OD from digital retinal images. This methodology uses morphological and edge detection techniques followed by the Circular Hough Transform to obtain a circular OD boundary approximation. It requires a pixel located within the OD as initial information. For this purpose, a location methodology based on a voting-type algorithm is also proposed. The algorithms were evaluated on the 1200 images of the publicly available MESSIDOR database. The location procedure succeeded in 99% of cases, taking an average computational time of 1.67 s. with a standard deviation of 0.14 s. On the other hand, the segmentation algorithm rendered an average common area overlapping between automated segmentations and true OD regions of 86%. The average computational time was 5.69 s with a standard deviation of 0.54 s. Moreover, a discussion on advantages and disadvantages of the models more generally used for OD segmentation is also presented in this paper.

/pdf/detecting-the-optic-disc-boundary-in-digital-fundus-images-owlq5w7env.pdf

Detecting the Optic Disc Boundary in Digital Fundus Images Using Morphological, Edge Detection, and Feature Extraction Techniques

Cancer of the breast.

Now-a-days medical fundus images are widely used in clinical diagnosis for the detection of retinal disorders. Fundus images are generally degraded by noise and suffer from low contrast issues. These issues make it difficult for ophthalmologist to detect and interpret diseases in fundus images. This paper presents a noise removal and contrast enhancement algorithm for fundus image. Integration of filters and contrast limited adaptive histogram equalization (CLAHE) technique is applied for solving the issues of de-noising and enhancement of color fundus image. The efficacy of the proposed method is evaluated through different performance parameters like Peak Signal to Noise Ratio (PSNR), Structural Similarity Index (SSIM), Correlation coefficient (CoC) and Edge preservation index (EPI). The proposed method achieved 7.85% improvement in PSNR, 1.19% improvement in SSIM, 0.12% improvement in CoC and 1.28% improvement in EPI when compared to the state of the art method.

An approach for de-noising and contrast enhancement of retinal fundus image using CLAHE

Computer-aided detection or diagnosis (CAD) has been a promising area of research over the last two decades. Medical image analysis aims to provide a more efficient diagnostic and treatment process for the radiologists and clinicians. However, with the development of science and technology, data interpretation manually in the conventional CAD systems has gradually become a challenging task. Deep learning methods, especially convolutional neural networks (CNNs), are successfully used as tools to solve this problem. This includes applications such as breast cancer diagnosis, lung nodule detection and prostate cancer localization. In this overview, the current state-of-the-art medical image analysis techniques in CAD research are presented, which focus on the convolutional neural network (CNN) based methods. The commonly used medical image databases in literature are also listed. It is anticipated that this paper can provide researchers in radiomics, precision medicine, and imaging grouping with a systematic picture of the CNN-based methods used in CAD research.

Convolutional neural networks for computer-aided detection or diagnosis in medical image analysis: An overview

A novel fuzzy logic and histogram based algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm is proposed for enhancing the local contrast of digital mammograms. A digital mammographic image uses a narrow range of gray levels. The contrast of a mammographic image distinguishes its diagnostic features such as masses and micro calcifications from one another with respect to the surrounding breast tissues. Thus, contrast enhancement and brightness preserving of digital mammograms is very important for early detection and further diagnosis of breast cancer. The limitation of existing contrast enhancement and brightness preserving techniques for enhancing digital mammograms is that they limit the amplification of contrast by clipping the histogram at a predefined clip-limit. This clip-limit is crisp and invariant to mammogram data. This causes all the pixels inside the window region of the mammogram to be equally affected. Hence these algorithms are not very suitable for real time diagnosis of breast cancer. In this paper, we propose a fuzzy logic and histogram based clipping algorithm called Fuzzy Clipped Contrast-Limited Adaptive Histogram Equalization (FC-CLAHE) algorithm, which automates the selection of the clip-limit that is relevant to the mammogram and enhances the local contrast of digital mammograms. The fuzzy inference system designed to automate the selection of clip-limit requires a limited number of control parameters. The fuzzy rules are developed to make the clip limit flexible and variant to mammogram data without human intervention. Experiments are conducted using the 322 digital mammograms extracted from MIAS database. The performance of the proposed technique is compared with various histogram equalization methods based on image quality measurement tools such as Contrast Improvement Index (CII), Discrete Entropy (DE), Absolute Mean Brightness Coefficient (AMBC) and Peak Signal-to-Noise Ratio (PSNR). Experimental results show that the proposed FC-CLAHE algorithm produces better results than several state-of-art algorithms.

Contrast enhancement and brightness preserving of digital mammograms using fuzzy clipped contrast-limited adaptive histogram equalization algorithm

Early detection of breast cancer in the mammograms is very essential in the field of medicine Contrast enhancement of mammograms based on Histogram Equalization (HE) is presented Histogram equalization is an effective and simple technique for contrast enhancement The standard histogram equalization (HE) usually results in excessive contrast enhancement because of lack of control on the level of enhancement The Histogram Modified Local Contrast Enhancement (HM-LCE) is introduced in this paper to adjust the level of contrast enhancement, which in turn gives the resultant image a strong contrast and also brings the local details present in the original image for more relevant interpretation It incorporates a two stage processing both histogram modifications as an optimization technique and a local contrast enhancement technique This method is tested for Mias mammogram images The performance of this method is determined using three parameters like Enhancement Measure (EME), Absolute Mean Brightness Error (AMBE) and Discrete Entropy (H) for all 22 numbers of Mias mammogram images with microcalcification It's enhancement potential is also tested by sobel and otsu methods for the detection of microcalcification in the mammogram image From the subjective and quantitative measures it is interesting that this proposed technique provides optimum results by giving better contrast enhancement and preserving the local information of the original mammogram images in the Mias data base and the method has increased the detectability of micro calcifications present in the given mammogram image

Short Communication: Histogram Modified Local Contrast Enhancement for mammogram images

Early detection of breast cancer in the mammograms is very essential in the field of medicine. Contrast enhancement for the detection of micro calcification of mammograms based on the Histogram Modified Contrast Limited Adaptive Histogram Equalization (HM-CLAHE) is presented. Histogram equalization is an effective and simple technique for contrast enhancement. The standard histogram equalization (HE) usually results in excessive contrast enhancement because of lack of control on the level of enhancement. The Histogram Modified Contrast Limited Adaptive Histogram Equalization (HM-CLAHE) is proposed in this paper to adjust the level of contrast enhancement, which in turn gives the resultant image a strong contrast and brings the local details for more relevant interpretation. It incorporates both histogram modifications as an optimization technique and Contrast Limited Adaptive Histogram Equalization. This method is tested for Mias mammogram images. The performance of this method is determined using the parameter like Enhancement Measure (EME). From the subjective and quantitative measures it is interesting that this proposed technique provides better contrast enhancement with preserving the local information of the mammogram images.

Histogram based contrast enhancement for mammogram images

An efficient detection of microcalcification based on edge enhancement using discrete wavelet transform (DWT) is presented in this paper. The proposed method is implemented by separating the wavelet coefficients into weak and strong edge coefficients for effective detection of microcalcification. Identification of strong and weak edge locations corresponding to microcalcification is obtained by allowing the input image through appropriate filters before wavelet decomposition. Before reconstructing the output image, the strong and weak edge coefficients are modified based on the energy of the coefficients. The reconstructed image exhibits a better enhancement with the fine detail components of microcalcification than the original mammogram image. Standard Mias mammogram database images and clinical mammograms are used for testing and comparing subjective and objective measures of the mammogram images. A comparative study is made with the existing state-of-the-art edge enhancement and contrast enhancement methods and results are encouraging. The edge emphasizing ability of the proposed method is highly proficient in detection of microcalcification from the mammogram.

Efficient edge emphasized mammogram image enhancement for detection of microcalcification

: In this paper, a new fusion based spectral enhancement of hyperspectral images is proposed. This method is designed to create a fused image with a special technique for fine-tuning. Fusion-based methods provide similar images without over-or underexposed any pixels. It contains spatial smoothness constraints for weight vectors. The solution of the optimum fusion-based approach is obtained from the modified Euler-Lagrangian equation.The visual presentation and several results confirm the effectiveness of the fusion-based technique.

Fusion Based Spectral Enhancement of Hyperspectral Images in Remote Sensing

- Land Cover classification in image processing is the task of classifying pixels whose spectral characteristics are similar and allocating them to the designated classification classes such as forests, grassland, wetlands and build-up areas. With the development of remote sensing technology and accuracy, the application of land cover images has become more widely spread. Many methods have achieved good classification results in the classification of land cover images. This paper discusses the issues and prospect by the combined effect of various methods and reviews the classification methods of remotely sensed images in statistical methods and recent machine learning methods.

Land Cover classification in remotely sensed data based on Deep Convolutional Neural Network

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