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Image fusion is used to extract relevant features from different image modalities like infrared and visual images and to combine them into a single image effectively. In this article, we have introduced a hybrid-parallel intuitionistic-near set based fusion scheme through near feature map approach. The proposed hybrid-parallel fusion scheme fully utilizes distributed memory parallelism and OpenMP for shared-memory parallelism. First, the fuzzy image representation based intuitionistic fuzzy theory is considered. Second, the principal features in the infrared and visual images are mapped using near-fuzzy set. Third, final fusion features are measured from decomposed multiple image blocks through domain decomposition strategy and image features are extracted via a defined probe function. After that, the near features have been computed from both the original images via intuitionistic entropy-based probe function, the ultimate fusion image is achieved through perceptual threshold limit on the membership grades in the fuzzy space. Finally, the resultant fused image is obtained through defuzzification. A hybrid MPI and OpenMP model is adopted to reduce inter-node communication and parallelized codes. The experimental result shows that the proposed method effectively combines the relevant information of both source images and provides a high resolution image. [ABSTRACT FROM AUTHOR]

This article represents a colonogram enhancement approach using intuitionistic fuzzy set (IFS) and fuzzy soft set (FSS) to improve the visual quality and highlight the local details in enhanced images without artifacts. The proposed method has the advantages of IFSs and fuzzy soft sets (FSSs) which can deal with gray‐level ambiguity in colonoscopy images. The combination of IFS and FSS works on intricate color variations with an adaptive parametric maps. First, the S‐membership function has been applied on the images to map into intuitionistic fuzzy space. We obtain intuitionistic fuzzy image from source image followed by decomposition of several blocks. Thereafter, the FSS is employed to get subsequent intuitionistic fuzzy soft matrix from individual image block. The FSS utilizes a class of parametric coefficients to obtain the hesitant score of individual pixels in each block through the soft‐score measure (SSM). Finally, the proposed method intensifies each membership degree in all blocks in the fuzzy plane using SSM and achieves an enhanced colonogram via defuzzification. Extensive experiment involving large data sets shows that the designed method exhibits better performance in contrast enhancement and visual quality improvement of colonograms for malformation detection (such as a polyps, malignant precancerous cells) in comparison with the state‐of‐art methods. [ABSTRACT FROM AUTHOR]

Summary: This article proposes a novel mammogram enhancement approach using adaptive intuitionistic fuzzy special set (IFSS) with deep convolutional neural network (called MECNNIFS) for visual interpretation of mammography lesions, lumps, and abnormal cells in low‐dose X‐ray images. The proposed MECNNIFS scheme utilizes the membership grade modification by IFSS on low‐dose X‐ray images (mammography). The suggested model attempts to increase the underexposed and abnormal structural regions such as breast lesions, lumps, and nodules on the mammogram. The proposed algorithm initially separates mammograms using convolutional neural networks (CNNs) into foreground and background areas and then fuzzifies the image by intuitionistic fuzzy set theory. Low‐level features of a mammogram of the adjacent part are integrated with CNN in pixel classification during the separation task stage to improve the performance. Hyperbolic regularization and hesitant score have been applied on fuzzy plane to quantify the uncertainty and fuzziness in spatial domain for the proposed contrast enhancement. Finally, an enhanced mammogram is acquired through the process of defuzzification. The results show better quality and performance for improvement of contrast and visual quality in mammograms compared with other state‐of‐the‐art methods. [ABSTRACT FROM AUTHOR]

In recent years, gene expression analysis has become crucial in studying microarray and provides several techniques related to computational biology and bioinformatics. This article introduces a novel Intuitionistic fuzzy set under rough multigranulation approximation based feature extraction from microarray gene expression dataset. The proposed model identifies cancer mediating human biomarkers using an Intuitionistic fuzzy soft set based similarity measure. Firstly, Intuitionistic fuzzy rough reduct is devised in the microarray datasets preceded by an entropy based pre-processing step. Intuitionistic fuzzy rough reduct produces the feature sets from the microarray datasets through roughness and accuracy measures that are differentially expressed in a cancerous state. Rough multigranulation approximations reduce the dimensions of the microarray datasets. Thereafter, a weighted similarity measure is designed using the intuitionistic fuzzy soft set for the classification of biomarkers having significant expression patterns from the normal state to the carcinogenic state. The proposed method is demonstrated on six microarray datasets and is validated with different performance metrics resulting in better effectiveness than the state-of-the-art methods. [Display omitted] • Intuitionistic fuzzy rough reduct is aimed for feature selection from microarray. • Initial preprocessing of microarray is made by entropy filtering. • The soft set based IFSSM classification model selects cancer responsible biomarkers. • Parameterization tool builds lower and upper approximations reducing the features. • The rough set deals with boundary regions precisely for fast convergence. • Biomarkers are validated with gene bank, NCBI, biochemical pathway databases. [ABSTRACT FROM AUTHOR]

• An intuitionistic fuzzy divergent measure based contrast enhancement approach. • Hesitant score metric is derived from source and complementary mammogram images. • Fuzzy membership grade is modified using hyperbolic regularization and entropy. • Enhanced mammogram is generated by applying Werner's AND/OR operators. • The model is computationally effective. In this article, we have proposed a novel mammogram image enhancement approach for visual interpretation of breast masses using an adaptive intuitionistic fuzzy divergent measure under hyperbolic regularization. The proposed scheme pursuits to raise the underexposed and abnormal growth of cells such as breast masses, deformity tissues, nodules, and lumps in mammogram images. In the first step, a complementary image has been generated from the source mammogram to separate object and background area. Thereafter, both source and background images are mapped into Intuitionistic Fuzzy Set (IFS) plane under Hyperbolic Regularization (HR). A novel entropy based intuitionistic fuzzy divergence measure has been designed using hyperbolic function for the membership modification in this work. In addition, fuzzy ambiguity correction distance function has been obtained from the hesitant score vectors from both source and complementary images. Werner's AND-OR has been utilized on both of them to generate modified membership function. Finally, we have achieved enhanced mammogram with visually improved breast masses through defuzzification process. It has been shown that the proposed method provides better result in comparison with the existing methods in terms of qualitative and higher score of image quantity measurement metrics such as SSIM (0.83), FQI (0.85), and IFQI (0.88). [ABSTRACT FROM AUTHOR]

Automated mammogram image segmentation is one of the most important methods in the domain of medical diagnosis and decision systems. Accurate segmentation of mammogram plays a key role for the detection of any kind of abnormality like lesion tissues, cyst in mammogram images for medical diagnosis. In this study, a novel hybrid soft computing entrenched segmentation method for mammograms is introduced for the detection of breast cancer in early stages. Here, we have designed a novel automatic mammogram segmentation method using intuitionistic fuzzy soft sets (IFSS) and Multigranulation rough set. First, the proposed clustering algorithm accomplishes a soft information structure from the source image using IFSSs via multiple fuzzy membership functions with Yager generating function. The IFSS handles the ambiguity among lesion and non-lesion pixels through the hesitant degree while shaping the membership function. To reduce distant pixels which do not belong to the region of interest (ROI), the lesion tissues in mammogram image is segregated by decision making scheme via a rough approximation of a fuzzy concept under the field of multigranulation space. Later, the proposed scheme utilizes soft-information builder with accuracy and roughness scores on multigranulation approximation space for segregation of normal and abnormal (lesion tissues) pixel from mammograms. The proposed model has generated a threshold image from accuracy and roughness scores via defuzzification process. The proposed segmentation model performs better than the existing methods using evaluation metrics like segmentation accuracy, Jeccards similarity coefficient. • Intuitionistic fuzzy soft rough set clustering is aimed for mammogram segmentation. • Initial centroids and clustering numbers are chosen by histogram methods. • The proposed IFSRCM clustering handles uncertain pixel precisely by hesitant score. • Parameterization tool builds lower and upper approximations reducing the features. • The rough set deals with boundary regions precisely for fast convergence. [ABSTRACT FROM AUTHOR]

Image enhancement algorithms are commonly used to increase the contrast and visual quality of low-dose x-ray images. This paper proposes an automated enhancement method using soft fuzzy sets with a new decision-making scheme based on Dempster-Shafer theory of evidence for the visual interpretation of pneumonia malformation in low-dose x-ray images, called as XEFSDS. The XEFSDS model first generates an original source x-ray image into a complementary image, then each original and complement image is applied to the characterized image object and background areas of fuzzy space. The S-function is utilized to define fuzzy soft sets for the classification of gray level ambiguity in both images, and hence a decision criterion via Dempster-Shafer approach and fuzzy interval has been adapted to discriminate uncertainties on the pixel intensity and the spatial information. Modified membership grade operations have been performed on each object/background area, and Werner's AND/OR operator (an aggregation operator) has been utilized to build a new membership function from two modified membership functions. Finally, an enhanced image is obtained from the new membership function via defuzzification. Experiments on different pneumonia X-ray images demonstrate that the XEFSDS scheme produces better results than the existing methods. To show the advantages of the XEFSDS scheme, we have executed a segmentation based examination on enhanced image for the detection of pneumonia malformation as well as abnormal lobe (lobar pneumonia) or bronchopneumonia. [ABSTRACT FROM AUTHOR]