Supervised Learning-Based Cell Image Segmentation for P53 Immunohistochemistry

This paper presents a segmentation approach for color natural images that performs an edge-preserving smoothing preprocess and a fuzzy-based region growing segmentation. The hue-chroma-intensity (HCI) color space is adopted for its perceptual uniformity. The edge-preserving smoothing preprocess is performed to smooth unnecessary details of an original image and obtain stable HCI information before transformation of color space and image segmentation. The proposed segmentation algorithm is performed in two processes: pixel-label assignment and region merging, which builds up fuzzy-based homogeneity measures for any two neighboring pixels, any pixel with its adjacent regions and any two adjacent regions by making a soft fusion of HCI information. We make some experiments to verify the necessity of the preprocess and the effectiveness of the proposed segmentation algorithm for rough segmentation of color natural images.

Diffusion coupling model based on neural oscillation synchronization theory, which can extract different targets with different gray levels from one, has been an important way for realizing image segmentation. This paper describes the diffusion coupling model and its algorithm and process for image segmentation. Simulation programs for supplementing this algorithm were compiled based on MATLAB platform. Experimental results on synthetic images, infrared images and brain magnetic resonance images show that, by setting reasonable algorithm parameters, the target image extraction and segmentation of different elements can be achieved. This image segmentation algorithm exhibits wide applications in medical image diagnosis, satellite image processing, traffic control, biometrics and others.

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.

In this article, we present an adaptive color similarity function defined in a modified hue‐saturation‐intensity color space, which can be used directly as a metric to obtain pixel‐wise segmentation of color images among other applications. The color information of every pixel is integrated as a unit by an adaptive similarity function thus avoiding color information scattering. As a direct application we present an efficient interactive, supervised color segmentation method with linear complexity respect to the number of pixels of the input image. The process has three steps: (1) Manual selection of few pixels in a sample of the color to be segmented. (2) Automatic generation of the so called color similarity image (CSI), which is a gray level image with all the gray level tonalities associated with the selected color. (3) Automatic threshold of the CSI to obtain the final segmentation. The proposed technique is direct, simple and computationally inexpensive. The evaluation of the efficiency of the color segmentation method is presented showing good performance in all cases of study. A comparative study is made between the behavior of the proposed method and two comparable segmentation techniques in color images using (1) the Euclidean metric of thea* andb* color channels rejectingL* and (2) a probabilistic approach ona* andb* in the CIE L*a*b* color space. Our testing system can be used either to explore the behavior of a similarity function (or metric) in different color spaces or to explore different metrics (or similarity functions) in the same color space. It was obtained from the results that the color parametersa* andb* are not independent of the luminance parameterL* as one might initially assume in the CIE L*a*b* color space. We show that our solution improves the quality of the proposed color segmentation technique and its quick result is significant with respect to other solutions found in the literature. The method also gives a good performance in low chromaticity, gray level and low contrast images. © 2016 Wiley Periodicals, Inc. Col Res Appl, 42, 156–172, 2017

A major problem in the automation of cervical cytology screening is the segmentation of cell images. This paper describes various standard segmentation methods plus one which determines a segmentation threshold based on the stability of the perimeter of the cell as the threshold is varied. As well as contour, certain structural information is used to decide upon the threshold which separates cytoplasm from the background. Once the cytoplasm threshold is found, cytoplasm and nucleus are separated by simple clustering into three groups, cytoplasm, folded cytoplasm and nucleus. These techniques have been tested on 1500 cervical cells that belong to one of eight normal classes and five abnormal classes. A minimum Mahalanobis distance classifier was used to compare results. Manually thresholded cells were classified correctly 66.0% of the time for the 13 class problem and 95.2% of the time on the two (normal-abnormal) class problem. The contour tracing technique was 52.9% and 90.0% correct, respectively.

This paper proposes a color image segmentation algorithm based on affinity propagation clustering. Firstly, the color image is converted to other color space; and then color image is sampled, and the sampling data is clustered by affinity propagation algorithm with given the number, then we can get their class label of the rest data according to the maximum similarity rules; finally, using the regional combined methods ,we can remove independent small regions, and get revised image segmentation result. The simulation experiment shows that this method can achieve the application of affinity propagation algorithm in large-scale color image segmentation. And it has a faster processing speed and satisfactory segmentation results, more in line with the human whole visual perception.

Color image has the potential to convey more information than monochrome or gray level images, RGB color model is used in many applications of image processing and image analysis such as Image Segmentation. The standard ways to deal with picture investigation and acknowledgment creatures by division of the picture into areas (articles) and figuring different properties and connections among these districts. Image segmentation algorithms, have been developed for extracting these regions. Important image segmentation is difficult to process due to the inherent noise and deterioration of the input indicators of the algorithm. The regions are not always specified, however, and it is often more fitting to consider them as fuzzy subjects in the image. In this work, an algorithm that is used to segment color images with clustering methods is defined. The lower conditions, results and results are illustrated. In the clustering algorithm, the results are compared using both Mahalanobis and Euclidean distances.

Image segmentation is an important stage for object recognition. Many methods have been proposed in the last few years for grayscale and color images. In this paper, we present a deep review of the state of the art on color image segmentation methods; through this paper, we explain the techniques based on edge detection, thresholding, histogram-thresholding, region, feature clustering and neural networks. Because color spaces play a key role in the methods reviewed, we also explain in detail the most commonly color spaces to represent and process colors. In addition, we present some important applications that use the methods of image segmentation reviewed. Finally, a set of metrics frequently used to evaluate quantitatively the segmented images is shown.

The most important properties of color spaces are without cross contamination between lightness, saturation and hue and with maximum perceptual uniformity after analyzing the deficiency of existing perception-based image processing problems. Unlike the traditional perception-based color spaces, this paper using multi-grid optimization and standard color-difference formulas defined a color lookup tables, constructs a new hue linear color space transformed images from an initial color space to the new color space. In order to verify the validity for image processing of the new space, we compared the color gamut mapping and image segmentation algorithms on the testing images with different color spaces. After comparing with the exiting color spaces, the experiment results showed color attributes of the new space free of cross contamination and with maximum perceptual uniformity which can improve the perception-based image processing algorithms accuracy.

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.

The purpose of the research is to develop a universal algorithm for partial texture segmentation of any visual images. The main peculiarity of the proposed segmentation procedure is the extraction of only homogeneous fine-grained texture segments present in the images. At first, an initial seed point is found for the largest and most homogeneous segment of the image. This initial seed point of the segment is expanded using a region growing method. Other texture segments of the image are extracted analogously in turn. At the second stage, the procedure of merging the extracted segments belonging to the same texture class is performed. Then, the detected texture segments are input to a neural network with competitive layers which accomplishes more accurate delineation of the shapes of the extracted texture segments. The proposed segmentation procedure is fully unsupervised, i.e., it does not use any a priori knowledge on either the type of textures or the number of texture segments in the image. The research results in development of the segmentation algorithm realized as a computer program tested in a series of experiments that demonstrate its efficiency on grayscale natural scenes.

This paper presents a color recognition and segmentation of color image through a combination of a multi-scale fuzzy system, an image edge based on Prewitt operator and morphological operators. In the proposed algorithm, after converting image from RGB color space to hue-saturation-value (HSV) color space, different scales of fuzzy sets in hue channel is constructed according to the people's psychological cognition to color. The image color would be quickly recognized fuzzily as less than ten kinds on scale one. To improve the performance of the color recognition rate, recognized image need to be identified on scale two to distinguish different colors observed by human eyes with the same color scheme. Then, adopting Prewitt operator to extract image edge information and applying a sequence of some morphological operators to the edge image, completing the multi-scale segmentation of image with the integration of color and edge information. Finally, indicating its area and species of specific color. The proposed algorithm can automatically adjusted in the process of color recognition and segmentation of image, it achieves recognition and segmentation without supervision. The obtained result has a good consistency with man's subjective visual perception.

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.

Three-dimensional (3D) prostate image segmentation is useful for cancer diagnosis and therapy guidance, but can be time-consuming to perform manually and involves varying levels of difficulty and interoperator variability within the prostatic base, midgland (MG), and apex. In this study, the authors measured accuracy and interobserver variability in the segmentation of the prostate on T2-weighted endorectal magnetic resonance (MR) imaging within the whole gland (WG), and separately within the apex, midgland, and base regions. The authors collected MR images from 42 prostate cancer patients. Prostate border delineation was performed manually by one observer on all images and by two other observers on a subset of ten images. The authors used complementary boundary-, region-, and volume-based metrics [mean absolute distance (MAD), Dice similarity coefficient (DSC), recall rate, precision rate, and volume difference (ΔV)] to elucidate the different types of segmentation errors that they observed. Evaluation for expert manual and semiautomatic segmentation approaches was carried out. Compared to manual segmentation, the authors' semiautomatic approach reduces the necessary user interaction by only requiring an indication of the anteroposterior orientation of the prostate and the selection of prostate center points on the apex, base, and midgland slices. Based on these inputs, the algorithm identifies candidate prostate boundary points using learned boundary appearance characteristics and performs regularization based on learned prostate shape information. The semiautomated algorithm required an average of 30 s of user interaction time (measured for nine operators) for each 3D prostate segmentation. The authors compared the segmentations from this method to manual segmentations in a single-operator (mean whole gland MAD = 2.0 mm, DSC = 82%, recall = 77%, precision = 88%, and ΔV = - 4.6 cm(3)) and multioperator study (mean whole gland MAD = 2.2 mm, DSC = 77%, recall = 72%, precision = 86%, and ΔV = - 4.0 cm(3)). These results compared favorably with observed differences between manual segmentations and a simultaneous truth and performance level estimation reference for this data set (whole gland differences as high as MAD = 3.1 mm, DSC = 78%, recall = 66%, precision = 77%, and ΔV = 15.5 cm(3)). The authors found that overall, midgland segmentation was more accurate and repeatable than the segmentation of the apex and base, with the base posing the greatest challenge. The main conclusions of this study were that (1) the semiautomated approach reduced interobserver segmentation variability; (2) the segmentation accuracy of the semiautomated approach, as well as the accuracies of recently published methods from other groups, were within the range of observed expert variability in manual prostate segmentation; and (3) further efforts in the development of computer-assisted segmentation would be most productive if focused on improvement of segmentation accuracy and reduction of variability within the prostatic apex and base.

Steganography is one of the methods used for secret communication. Steganography attempts to hide the existence of the information. The object used to hide the secret information is called as cover object. Images are the most popular cover objects used for steganography. Different techniques have to be used for color image steganography and grey scale image steganography since they are stored in different ways. Color image are normally stored with 24 bit depth and grey scale images are stored with 8 bit depth. Color images can hold large amount of secret information since they have three color components. Different color spaces namely RGB (Red Green Blue), HSV (Hue, Saturation, Value), YUV, YIQ, YCbCr (Luminance, hrominance) etc. are used to represent color images. Color image steganography can be done in any color space domain. In this paper color image steganography in RGB and YCbCr domain are compared. The secret information considered is grey scale image. Since RGB is the common method of representation, hiding secret information in this format is not secure.