Histogram equalization

About: Histogram equalization is a research topic. Over the lifetime, 5755 publications have been published within this topic receiving 89313 citations.

A method for normalizing pathology images to improve feature extraction for quantitative pathology.

Abstract: Purpose: With the advent of digital slide scanning technologies and the potential proliferation of large repositories of digital pathology images, many research studies can leverage these data for biomedical discovery and to develop clinical applications. However, quantitative analysis of digital pathology images is impeded by batch effects generated by varied staining protocols and staining conditions of pathological slides. Methods: To overcome this problem, this paper proposes a novel, fully automated stain normalization method to reduce batch effects and thus aid research in digital pathology applications. Their method, intensity centering and histogram equalization (ICHE), normalizes a diverse set of pathology images by first scaling the centroids of the intensity histograms to a common point and then applying a modified version of contrast-limited adaptive histogram equalization. Normalization was performed on two datasets of digitized hematoxylin and eosin (H&E) slides of different tissue slices from the same lung tumor, and one immunohistochemistry dataset of digitized slides created by restaining one of the H&E datasets. Results: The ICHE method was evaluated based on image intensity values, quantitative features, and the effect on downstream applications, such as a computer aided diagnosis. For comparison, three methods from the literature were reimplemented and evaluated using the same criteria. The authors found that ICHE not only improved performance compared with un-normalized images, but in most cases showed improvement compared with previous methods for correcting batch effects in the literature. Conclusions: ICHE may be a useful preprocessing step a digital pathology image processing pipeline.

Multi-View Ensemble Convolutional Neural Network to Improve Classification of Pneumonia in Low Contrast Chest X-Ray Images

Abstract: Pneumonia is one of the leading causes of childhood mortality worldwide. Chest x-ray (CXR) can aid the diagnosis of pneumonia, but in the case of low contrast images, it is important to include computational tools to aid specialists. Deep learning is an alternative because it can identify patterns automatically, even in low-resolution images. We propose herein a convolutional neural network (CNN) architecture with different training strategies towards detecting pneumonia on CXRs and distinguishing its subforms of bacteria and virus. We also evaluated different image pre-processing methods to improve the classification. This study used CXRs from pediatric patients from a public pneumonia CXR dataset. The pre-processing methods evaluated were image cropping and histogram equalization. To classify the images, we adopted the VGG16 CNN and replaced its fully-connected layers with a customized multilayer perceptron. With this architecture, we proposed and evaluated four different training strategies: original CXR image (baseline), chest-cavity-cropped image (A), and histogram-equalized segmented image (B). The last strategy method (C) implemented is based on ensemble between strategies A and B. The performance was assessed by the area under the ROC curve (AUC) with 95% confidence interval (CI), accuracy, sensitivity, specificity, and F1-score. The ensemble model C yielded the highest performances: AUC of 0.97 (CI: 0.96–0.99) to classify pneumonia vs. normal, and AUC of 0.91 (CI: 0.88–0.94) to classify bacterial vs. viral cases. All models that used pre-processed images showed higher AUC than baseline, which used the original CXR image. Image cropping and histogram equalization reduced irrelevant information from the exam, enhanced contrast, and was able to identify fine CXR texture details. The proposed ensemble model increased the representation of inflammatory patterns from bacteria and viruses with few epochs to train the deep CNNs.Clinical relevance— Deep learning can identify complex radiographic patterns in low contrast images due to pneumonia and distinguish its subforms of bacteria and virus. The correlation of imaging with lab results could accelerate the adoption of complementary exams to confirm the disease’s cause

Vector angular distance measure for indexing and retrieval of color

Abstract: A key aspect of image retrieval using color, is the creation of robust and efficient indices. In particular, the color histogram remains the most popular index, due primarily to its simplicity. However, the color histogram has a number of drawbacks. Specifically, histograms capture only global activity, they require quantization to reduce dimensionality, are highly dependent on the chosen color space, have no means to exclude a certain color from a query, and can provide erroneous results due to gamma nonlinearity. In this paper, we present a vector angular distance measure, which is implemented as part of our database system. Our system does away with histogram techniques for color indexing and retrieval, and implements color vector techniques. We use color segmentation to extract regions of prominent color and use representative vectors from these extracted regions in the image indices. We therefore reach a much smaller index, which does not have the granularity of a histogram. Rather, similarity is based on our vector angular distance measure, between a query color vector and the indexed representative vectors.

Fighting Detection Based on Optical Flow Context Histogram

Abstract: This paper proposes a new feature, optical flow context histogram (OFCH) for detecting abnormal events, especially the fighting violence events from a live camera stream. The optical flow context histogram is a log-polar histogram system which combines the histogram of orientation and magnitude of optical flow together. The human action is represented by using the histogram sequence of orientation and magnitude of optical flow. PCA is adopted to reduce the dimension of the human action representation. Several machine learning methods, including random forest, support vector machine and Bayesnet are employed for sequence classification. The experiments were carried out on the video clips downloaded from the Internet. The results show that the proposed methods work well when using a fixed surveillance camera.

Application of QIM with dead zone for histogram preserving JPEG steganography

Abstract: This paper presents two histogram preserving JPEG steganographic methods aiming at secure JPEG steganography against histogram-based attacks. The first one is a histogram quasi-preserving method, which uses quantization index modulation (QIM) at quantization step of DCT coefficients. Since a straightforward application of QIM causes a significant histogram change, a device is introduced in order not to change the after-embedding histogram excessively. The second one is a histogram preserving method based on histogram matching using two quantizers with a dead zone. In comparison with F5 as a representative JPEG steganography, the two methods show high performance with regard to embedding rate, PSNR of stego image, and particularly histogram preservation.