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The development of digital pathology offers a significant opportunity to evaluate and analyze the whole slides of disease tissue effectively. In particular, the segmentation of nuclei from histopathology images plays an important role in quantitatively measuring and evaluating the acquired diseased tissue. There are many automatic methods to segment cell nuclei in histopathology images. One widely used unsupervised segmentation approach is based on standard k-means or fuzzy c-means (FCM) to process the color histopathology images to segment cell nuclei. Compared with the supervised learning method, this approach can obtain segmented nuclei without annotated nuclei labels for training, which saves a lot of labeling and training time. The color space and $k$ value among this method plays a crucial role in determining the nuclei segmentation performance. However, few works have investigated various color spaces and $k$ value selection simultaneously in unsupervised color-based nuclei segmentation with $k$-means or FCM algorithms. In this study, we will present color-based nuclei segmentation methods with standard $k$-means and FCM algorithms for histopathology images. Several color spaces of Haematoxylin and Eosin (H&E) stained histopathology data and various $k$ values among $k$-means and FCM are investigated correspondingly to explore the suitable selection for nuclei segmentation. A comprehensive nuclei dataset with 7 different organs is used to validate our proposed method. Related experimental results indicate that $L^{\ast }{a}^{\ast }{b}^{\ast }$ and the YCbCr color spaces with a $k$ of 4 are more reasonable for nuclei segmentation via $k$-means, while the $L^{\ast }{a}^{\ast }{b}^{\ast }$ color space with $k$ of 4 is useful via FCM.

Content-based face image retrieval is concerned with computer retrieval of face images (of a given subject) based on the geometric or statistical features automatically derived from these images. It is well known that color spaces provide powerful information for image indexing and retrieval by means of color invariants, color histogram, color texture, etc. This paper assesses comparatively the performance of content-based face image retrieval in different color spaces using a standard algorithm, the Principal Component Analysis (PCA), which has become a popular algorithm in the face recognition community. In particular, we comparatively assess 12 color spaces (RGB, HSV, YUV, YCbCr, XYZ, YIQ, L*a*b*, U*V*W*, L*u*v*, I₁I₂I₃, HSI, and rgb) by evaluating seven color configurations for every single color space. A color configuration is defined by an individual or a combination of color component images. Take the RGB color space as an example, possible color configurations are R, G, B, RG, RB, GB and RGB. Experimental results using 600 FERET color images corresponding to 200 subjects and 456 FRGC (Face Recognition Grand Challenge) color images of 152 subjects show that some color configurations, such as YV in the YUV color space and YI in the YIQ color space, help improve face retrieval performance.

This paper presents a new algorithm for detection and compensation of backlight images. The proposed technique attacks the weakness of the conventional backlight image processing methods such as over-saturation, losing contrast and so on. The proposed algorithm consists of two operation phases: detection and compensation phases. In the detection phase, we use the spatial position characteristic and histogram of backlight image to obtain two image indices, which can determine the backlight degree of an image. Fuzzy logic is then used to integrate these two indices into a final backlight index determining the final backlight degree of an image precisely. Second, in the compensation phase, to solve the over-saturation problem that exists usually in conventional image compensation methods, we propose the adaptive compensation-curve scheme to compensate and enhance the brightness of backlight images. The luminance of a backlight image is adjusted according to the compensation curve, which is adapted dynamically according to the backlight degree indicated by the backlight index estimated in the detection phase. The performance of the proposed technique is tested on 100 backlight images covering various kinds of backlight conditions and degrees. The experimental and comparison results clearly show the superiority of the proposed technique.

In this paper, a two-stage content-based image retrieval (CBIR) approach is proposed to improve the retrieval performance. To develop a general retrieval scheme which is less dependent on domain-specific knowledge, the discrete cosine transform (DCT) is employed as a feature extraction method. In establishing the database, the DC coefficients of Y, U and V components are quantized such that the feature space is partitioned into a finite number of grids, each of which is mapped to a grid code (GC). When querying an image, at coarse classification stage, the grid-based classification (GBC) and the distance threshold pruning (DTP) serve as a filter to remove those candidates with widely distinct features. At the fine classification stage, only the remaining candidates need to be computed for the detailed similarity comparison. The experimental results show that both high efficacy and high efficiency can be achieved simultaneously using the proposed two-stage approach.

To improve contrast and restore color for underwater images without suffering from insufficient details and color cast, this paper proposes a fusion algorithm for different color spaces based on contrast limited adaptive histogram equalization (CLAHE). The original color image is first converted from RGB space to two different spaces: YIQ and HSI. Then, the algorithm separately applies CLAHE in YIQ and HSI color spaces to obtain two different enhanced images. After that, the YIQ and HSI enhanced images are respectively converted back to RGB space. When the three components of red, green, and blue are not coherent in the YIQ-RGB or HSI-RGB images, the three components will have to be harmonized with the CLAHE algorithm in RGB space. Finally, using a 4-direction Sobel edge detector in the bounded general logarithm ratio operation, a self-adaptive weight selection nonlinear image enhancement is carried out to fuse the YIQ-RGB and HSI-RGB images together to achieve the final image. The experimental results showed that the proposed algorithm provided more detail enhancement and higher values of color restoration than other image enhancement algorithms. The proposed algorithm can effectively reduce noise interference and observably improve the image quality for underwater images.

Lip segmentation is one of critical steps in a lip-reading system, because it closely relates to the accuracy of system recognition. In this paper, we aim to improve the accuracy of lip segmentation. A novel color space is proposed which consists of the $U$ component in the CIE-LUV space and the sum of $C$2 and $C$3 components of the image after discrete Hartley transform (DHT). We select a rhombus as the initial contour as its shape is approximate to a closed lip shape relatively. These notions are achieved based on the method of the Active contour model. The active contour model (ACM) is performed by the Chan–Vese model, and the result of each component is gained separately. Finally, the ultimate results are obtained by merging the result of each component together. Through experiments we can get a conclusion that this method can get more accurate and smoother lip contour. Meanwhile, the proposed method is more efficient compared with the classic ACM because it avoids some problems in the classic active contour model, like the radius of the initial contour needs to be set manually according to the size of images.

Fluctuations in parameters of light-emitting diode (LED) electromechanical 3D–display are investigated. It is shown, that there are two types of fluctuations in the rotating 3D–display. The first one is caused by a small increment in the rotation angle, which has a tendency to the increase. That occurs in the form of the “drift” without periodic changes of the angle. The second one is the change in small linear increments of the angle, which occurs as undamped harmonic oscillations with constant amplitude. This shows the stability of the investigated steady state because there is no tendency to increase the amplitude of the considered parameter regime. In conclusion we give some recommendations how to improve synchronization of the system.

The severity and outcome of mastocytosis are known to be related to the number and size of mast cells. The standard treatment of this rare dermatological disorder is phototherapy with UVA1 irradiation. Here, the treatment effects were evaluated during the course of phototherapy on a patient with mastocytosis (urticaria pigmentosa). Photomicrographs of CD117 antibody-stained histological sections of the dermis were analyzed using image software programmed with artificial neural networks. The artificial neural networks analyzed the color feature of epidermis, dermis and mast cells. First, mast cells were isolated from epidermis and dermis, and RGB pixels were converted to HSV color space for artificial neural networks training. Secondly, morphology and connective component labeling were used in epidermal pixels classification to determine the scope of dermal areas. Thirdly, regional separation isolated the dermis for the mast cell analysis. Specifically, the number and size of mast cells were quantified. The method has good reproducibility compared with other methods which had larger variance and lower accuracy. We found also that the UVA1 phototherapy was effective as indicated by a post-treatment change in the ratio of mast cell surface to cell number. In addition, we compared similar results based on subjective visual inspection (from 10 human subjects). Both methods of analysis showed a reduction in the number and size of mast cells after phototherapy. But compared with human judgment, the computerized method was superior in accuracy, sensitivity, specificity and reproducibility of the findings.