Enhanced Color Image Segmentation by Graph Cut Method in General and Medical Images

Segmentation is a low-level processing aimed at the partition of an image in disjoint regions, each one homogeneous with respect to some properties like intensity, texture, shape, etc. Such a task is needed in many high-level processing and applications in such diverse fields as remote-sensing, medical imaging, video coding, and industrial automation, just to name a few. In this work of thesis, we first deal with the problem of color based image segmentation, that is based on some form of homogeneity in the color/spectral properties of the image, and then move to the more complex task of texture based segmentation, where the aim is to recognize complex structures in the image which are typically non homogeneous in terms of spectral properties. Concerning color based segmentation, we focus on a specific class of Markov Random Field (MRF) probability models, namely the Tree-Structured Markov Random Fields (TS-MRF), recently proposed to provide a hierarchical multiscale description of images. Based on such a model, the unsupervised image segmentation is carried out by means of a sequence of nested class splits, where each class is modeled as a local binary MRF. As a first contribution, we first extend the model to generic tree structures removing the unnecessary binary constraint, and then devise a new TS-MRF unsupervised segmentation technique which improves upon the original algorithm by selecting a better tree structure and eliminating spurious classes. Such results are obtained by using the Mean-Shift procedure to estimate the number of pdf modes at each node, and to obtain a more reliable initial clustering for subsequent MRF optimization. To this end, we devise a new reliable and fast clustering algorithm based on the Mean-Shift technique, which makes use of a variable-bandwidth strategy based on the k-Nearest Neighbors (k-NN) technique, and is implemented with a speed-up strategy that cuts significantly the computational complexity. Experimental results on synthetic data and real life images from the remote sensing domain prove the potential of the proposed method. Turning to texture based segmentation, we present a novel multiscale texture model, and a related algorithm for the unsupervised segmentation of color images. Elementary textures are characterized by their spatial interactions with neighboring regions along selected directions. Such interactions are modeled in turn by means of a set of Markov chains, one for each direction, whose parameters are collected in a feature vector that synthetically describes the texture. Based on the feature vectors, the texture are then recursively merged, giving rise to larger and more complex textures, which appear at different scales of observation: accordingly, the model is named Hierarchical Multiple Markov Chain (H-MMC). The Texture Fragmentation and Reconstruction (TFR) algorithm, addresses the unsupervised segmentation problem based on the H-MMC model. The ``fragmentation'' step allows one to find the elementary textures of the model, while the ``reconstruction'' step defines the hierarchical image segmentation based on a probabilistic measure (texture score) which takes into account both region scale and inter-region interactions. The performance of the proposed method was assessed through the Prague segmentation benchmark, based on mosaics of real natural textures, and also tested on real-world natural images from the Berkeley dataset. Finally, an application of the TFR algorithm for the unsupervised segmentation of multiresolution remotely sensed images is proposed, based on the significant presence of textural content in last generation high-resolution satellite images. The obtained results further assess the potential of the proposed method for such real life applications.

The topic of colour image segmentation has been and still is a hot issue in areas such as computer vision and image processing because of its wide range of practical applications. The urge has led to the development of numerous colour image segmentation algorithms to extract salient objects from colour images. However, because of the diverse imaging conditions in varying application domains, accuracy and robustness of several state-of-the-art colour image segmentation algorithms still leave room for further improvement. This dissertation reports on the development of a new image segmentation algorithm based on perceptual colour difference saliency along with binary morphological operations. The algorithm consists of four essential processing stages which are colour image transformation, luminance image enhancement, salient pixel computation and image artefact filtering. The input RGB colour image is first transformed into the CIE L*a*b colour image to achieve perceptual saliency and obtain the best possible calibration of the transformation model. The luminance channel of the transformed colour image is then enhanced using an adaptive gamma correction function to alleviate the adverse effects of illumination variation, low contrast and improve the image quality significantly. The salient objects in the input colour image are then determined by calculating saliency at each pixel in order to preserve spatial information. The computed saliency map is then filtered using the morphological operations to eliminate undesired factors that are likely present in the colour image. A series of experiments was performed to evaluate the effectiveness of the new perceptual colour difference saliency algorithm for colour image segmentation. This was accomplished by testing the algorithm on a large set of a hundred and ninety images acquired from four distinct publicly available benchmarks corporal. The accuracy of the developed colour image segmentation algorithm was quantified using four widely used statistical evaluation metrics in terms of precision, F-measure, error and Dice. Promising results were obtained despite the fact that the experimental images were selected from four different corporal and in varying imaging conditions. The results have indeed demonstrated that the performance of the newly developed colour image segmentation algorithm is consistent with an improved performance compared to a number of other saliency and non- saliency state-of-the-art image segmentation algorithms.

This chapter is focused on spatially adaptive image processing for color images in the context of the General Adaptive Neighborhood Image Processing (GANIP) approach. The GANIP was first defined for gray-tone images and is here extended to color images. A set of local adaptive neighborhoods is defined for each image point, depending on the color intensity function of the image. These adaptive neighborhoods are then used as spatially adaptive operational windows for defining adaptive Choquet filters and adaptive morphological filters. The resulting adaptive operators are successfully applied and compared with the classical operators for image restoration, enhancement and segmentation of color images.

A.P. Dhawan, H.K. Huang, D.-S. Kim, eds. World Scientific Publishing; 2008, 868 pages, $228.00. At first glance at the cover and size of Principles and Advanced Methods in Medical Imaging and Image Analysis , a passerby might think that it is the latest Harry Potter novel; however, the gadgets and

Medical imaging is becoming indispensable for patients' healthcare. Machine learning plays an essential role in the medical imaging field, including computer-aided diagnosis, image segmentation, image registration, image fusion, image-guided therapy, image annotation, and image database retrieval. With advances in medical imaging, new imaging modalities and methodologies such as cone-beam/multislice CT, 3D ultrasound imaging, tomosynthesis, diffusion-weighted magnetic resonance imaging (MRI), positron-emission tomography (PET)/CT, electrical impedance tomography, and diffuse optical tomography, new machine-learning algorithms/applications are demanded in the medical imaging field. Because of large variations and complexityit is generally difficult to derive analytic solutions or simple equations to represent objects such as lesions and anatomy in medical images. Therefore, tasks in medical imaging require “learning from examples” for accurate representation of data and prior knowledge. Because of its essential needs, machine learning in medical imaging is one of the most promising, growing fields. The main aim of this special issue is to help advance the scientific research within the broad field of machine learning in medical imaging. The special issue was planned in conjunction with the International Workshop on Machine Learning in Medical Imaging (MLMI 2010) [1], which was the first workshop on this topic, held at the 13th International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI 2010) in September, 2010, in Beijing, China. This special issue is one in a series of special issues of journals on this topic [2]; it focuses on major trends and challenges in this area, and it presents work aimed at identifying new cutting-edge techniques and their use in medical imaging. The quality level of the submissions for this special issue was very high. A total of 17 papers were submitted to this issue in response to the call for papers. Based on a rigorous review process, 10 papers (59%) were accepted for publication in the special issue. The special issue starts by a review of studies on a class of machine-learning techniques, called pixel/voxel-based machine learning, in medical imaging by K. Suzuki. A series of medical imaging applications of machine-learning techniques are presented. A large variety of applications are well represented here, including organ modeling by D. Wang et al. and X. Qiao and Y.-W. Chen, brain function estimation by V. Michel et al., image reconstruction by H. Shouno et al., lesion classification by P. Wighton et al., modality classification by X.-H. Han and Y.-W. Chen, lesion segmentation by M. Zortea et al., organ segmentation by S. Alzubi et al., and visualization of molecular signals by F. Mattoli et al. Also, the issue covers various biomedical imaging modalities, including MRI by D. Wang et al., CT by X. Qiao and Y.-W. Chen and H. Shouno et al., functional MRI by V. Michel et al., dermoscopy by P. Wighton et al. and M. Zortea et al., scintigraphy by X.-H. Han and Y.-W. Chen, ultrasound imaging by X.-H. Han and Y.-W. Chen, radiography by X.-H. Han and Y.-W. Chen, MR angiography by S. Alzubi et al., and microscopy by F. Mattoli et al. as well as a variety of organs, including the kidneys by D. Wang et al., liver by X. Qiao and Y.-W. Chen, brain by V. Michel et al. and F. Mattoli et al., chest by S. Alzubi et al., skin by P. Wighton et al. and M. Zortea et al., and heart by F. Mattoli et al. Various machine-learning techniques were developed/used to solve the respective problems, including structured dictionary learning by D. Wang et al., generalized N-dimensional principal component analysis by X. Qiao et al., multiclass sparse Bayesian regression by V. Michel et al., Bayesian hyperparameter inference by H. Shouno et al., supervised learning of probabilistic models based on maximum aposteriori estimation and conditional random fields by P. Wighton et al., joint kernel equal contribution in support vector classification by X.-H. Han and Y.-W. Chen, and iterative hybrid classification by M. Zortea et al. We are grateful to all authors for their excellent contributions to this special issue and to all reviewers for their reviews and constructive suggestions. We hope that this special issue will inspire further ideas for creative research, advance the field of machine learning in medical imaging, and facilitate the translation of the research from bench to bedside. Kenji Suzuki Pingkun Yan Fei Wang Dinggang Shen

segmentation divides an image into several constituent components such as color, structure, shape, and texture. It forms a major research topic for many image processing researchers as the applications are endless. Its applications include image enhancement, object detection, image retrieval, image compression, and medical image processing to name a few. The segmentation of color images is necessary for efficient pattern recognition and feature extraction involving various color spaces such as RGB, HSV and CIE L*A*B*, etc. This paper describes the different cluster based segmentation techniques used for segmenting the different color images and the resultant is analyzed with subjective and objective measures. Initially, registered color images are considered as input. Then the cluster based segmentation techniques namely K-Means clustering, Pillar-Kmeans clustering and Fuzzy C-means (FCM) clustering techniques are applied. Further, the segmented image is analyzed with measures such as compactness and execution time. From the experimental results, it has been observed that K-means and Pillar-Kmeans are the most suitable techniques for RGB, HSV and LAB color spaces than the FCM technique.

Color image enhancement refers to improving the visual quality of color images by adjusting various attributes such as brightness, contrast, sharpness, and color balance. This enhancement aims to make the images more visually appealing, precise, and easier to interpret, often by accentuating essential features or details while minimizing noise or distortion. Techniques vary from simple adjustments of brightness and contrast to sophisticated algorithms. Effective image enhancement is crucial in numerous fields like medical imaging; enhancing the medical image is vital for accurate diagnosis and treatment planning, as well as remote sensing for improved satellite and aerial imagery. In security and surveillance, enhanced clarity in footage from surveillance cameras, especially in low-light scenarios, is crucial for identifying subjects and activities. However, image enhancement faces several challenges, like noise amplification and Over-enhancement, leading to unnatural-looking images with exaggerated or distorted features. In this paper, low-exposed or night color images are considered for enhancement, and this paper introduces exposure-based recursive histogram equalization techniques along with an Energy Curve instead of the conventional histogram; the energy curve is similar to a histogram based on spatial Contextual Information of an image. The proposed methods are Recursive Exposure-based Sub-image Histogram Equalization, Recursively Separated Exposure-based Sub-image Histogram Equalization, and Exposure-based Sub-image Histogram Equalization techniques considering spatial contextual information of images using an Energy Curve to improve results. These methods were tested on several publicly available datasetswith low-contrast color images and compared with HE, BBHE, DSHE, CLAHE, ESIHE, R-ESIHE, and RS-ESIHE. The proposed techniques are evaluated using parameterslikeAMBE, PSNR, MSE, Entropy, SSIM, and FSIM. The average PSNR values for eight images (taken from TeV DICeneter https://tev.fbk.eu/resources/imageenhancement) using the techniques mentioned above were 9.10817, 20.80568, 12.82645, 10.39347, 16.27458, 15.19979, and 14.59595, respectively. In contrast, the proposed ESIHE with Energy Curve achieved a PSNR of 22.31585, outperforming other methods across multiple metrics, particularly excelling in noise reduction (PSNR), error minimization (MSE: 193.44), structural similarity (SSIM: 0.733319), and feature retention (FSIM: 0.917678). The comparison demonstrates that enhancement methods can be significantly improved by considering spatial contextual information.

Block-based perceptual encryption (PE) algorithms are becoming popular for multimedia data protection because of their low computational demands and format-compliancy with the JPEG standard. In conventional methods, a colored image as an input is a prerequisite to enable smaller block size for better security. However, in domains such as medical image processing, unavailability of color images makes PE methods inadequate for their secure transmission and storage. Therefore, this study proposes a PE method that is applicable for both color and grayscale images. In the proposed method, efficiency is achieved by considering smaller block size in encryption steps that have negligible effect on the compressibility of an image. The analyses have shown that the proposed system offers better security with only 12% more bitrate requirement as opposed to 113% in conventional methods. As an application of the proposed method, we have considered a smart hospital that avails healthcare cloud services to outsource their deep learning (DL) computations and storage needs. The EfficientNetV2-based model is implemented for automatic tuberculosis (TB) diagnosis in chest X-ray images. In addition, we have proposed noise-based data augmentation method to address data deficiency in medical image analysis. As a result, the model accuracy was improved by 10%.

Multilevel thresholding for segmentation of medical brain images using real coded genetic algorithm

Along with the development of medical imaging technology, medical image analysis has become a crucial part of clinical diagnosis. However, traditional image analysis methods rely on manual feature extraction and expert evaluation, triggering problems such as low efficiency and lack of accuracy. Deep learning techniques, especially convolutional neural networks (CNNs), have made significant breakthroughs in medical image analysis in recent years. The aim of this paper is to study the application of deep learning in medical imaging, and explore the advantages and development potential in early disease diagnosis, image segmentation and feature extraction. This paper reviews the basic principles of deep learning and analyses the applicability of deep learning in medical imaging by combining the characteristics of medical imaging. Through systematic analysis of existing literature, this paper summarises the remarkable achievements of deep learning in processing, analysing and applying it to various medical images, such as Optical Coherence Tomography (OCT), Magnetic Resonance Imaging (MRI) and so on. It has been shown that deep learning methods can significantly improve the accuracy and efficiency of medical image processing and demonstrate performance due to traditional methods in tasks such as classification. However, how to improve the interpretability of models and the generalisability of clinical applications are still current research hotspots and challenges.

Natural color image enhancement and evaluation algorithm based on human visual system

Background and Objectives: Brain Tumor Fusion-based Segments and Classification-Non-enhancing tumor (BTFSC-Net) is a hybrid system for classifying brain tumors that combine medical image fusion, segmentation, feature extraction, and classification procedures. Materials and Methods: to reduce noise from medical images, the hybrid probabilistic wiener filter (HPWF) is first applied as a preprocessing step. Then, to combine robust edge analysis (REA) properties in magnetic resonance imaging (MRI) and computed tomography (CT) medical images, a fusion network based on deep learning convolutional neural networks (DLCNN) is developed. Here, the brain images’ slopes and borders are detected using REA. To separate the sick region from the color image, adaptive fuzzy c-means integrated k-means (HFCMIK) clustering is then implemented. To extract hybrid features from the fused image, low-level features based on the redundant discrete wavelet transform (RDWT), empirical color features, and texture characteristics based on the gray-level cooccurrence matrix (GLCM) are also used. Finally, to distinguish between benign and malignant tumors, a deep learning probabilistic neural network (DLPNN) is deployed. Results: according to the findings, the suggested BTFSC-Net model performed better than more traditional preprocessing, fusion, segmentation, and classification techniques. Additionally, 99.21% segmentation accuracy and 99.46% classification accuracy were reached using the proposed BTFSC-Net model. Conclusions: earlier approaches have not performed as well as our presented method for image fusion, segmentation, feature extraction, classification operations, and brain tumor classification. These results illustrate that the designed approach performed more effectively in terms of enhanced quantitative evaluation with better accuracy as well as visual performance.

Machine learning techniques such as deep learning methods have produced promising results in medical images analysis. This work proposes a user-friendly system that utilizes deep learning techniques for detecting and diagnosing diseases using medical images. This includes the design of CAD-based project that can reduce human factor-related errors while performing manual screening of medical images. The system accepts medical images as input and performs segmentation of the images. Segmentation process analyzes and identifies the region of interest (ROI) of diseases from medical images. Analyzing and segmentation of medical images has assisted in the diagnosis and monitoring of some diseases. Diseases such as skin cancer, age-related fovea degeneration, diabetic retinopathy, glaucoma, hypertension, arteriosclerosis, and choroidal neovascularization can be effectively managed by the analysis of skin lesion and retinal vessels images. The proposed system was evaluated on diseases such as diabetic retinopathy from retina images and skin cancer from dermoscopic images.

Segmentation involves separating an object from the background in a given image. Image segmentation has a variety of applications and has received considerable attention in multimedia application and computer vision. Although numerous approaches have been introduced, image segmentation is still far from being solved due to most of image segmentation algorithms are often so complicated and some unsatisfactory results appear frequently. Therefore, developing a suitable technique of image segmentation is still a challenging problem. In this article, a novel color image segmentation will be introduced based on quaternion radial harmonic Fourier moments (QRHFMs) and proximal classifier. Firstly, the image feature of pixel-level is represented by the accurate and invariant QRHFMs holistically as a vector field, which can describe sufficiently the image pixel information due to take into account the relationship among different color channels. Secondly, the image feature from pixel-level is utilized as the input of the proximal classifier with consistency (PCC), which not only has lower computation time but also has better generalization compared to traditional support vector machines classifiers. Then, we choose the training samples by Tsallis entropy thresholding to train PCC classification model. Finally, the color image is classified by the trained PCC classification model. Our algorithm can make full use of the accurate and robust local image feature, as well the quickness and generalization ability of PCC classifier. A series of experimental results shows that this algorithm has better segmentation performance than the state-of-the-art method from the literature.

Image segmentation is the process of dividing an image into homogenous regions. It is an essential step towards high-level image processing task such as image analysis, pattern recognition and computer vision. Processing of color images has become an important issue due to its huge usage in computer vision applications. It is observed that most of the color image segmentation techniques are derived from monochrome image segmentation. The techniques for segmentation of monochrome images are based on the principles of histogram thresholding, edge detection, region growing etc. Many color image segmentation algorithms using different color models and these principles are proposed. Extraction of objects within an image without a prior knowledge is one of the important issues in segmentation area. Novel approaches such as fuzzy set theory, neural network and neuro-fuzzy based segmentation are coming up to tackle this problem. This paper is an endeavor to review various algorithms and recent advances in color image segmentation.