Bioconjugated quantum dots (QDs) provide a new class of biological labels for evaluating biomolecular signatures (biomarkers) on intact cells and tissue specimens. In particular, the use of multicolor QD probes in immunohistochemistry is considered one of the most important and clinically relevant applications. At present, however, clinical applications of QD-based immunohistochemistry have achieved only limited success. A major bottleneck is the lack of robust protocols to define the key parameters and steps. Here, we describe our recent experience, preliminary results and detailed protocols for QD–antibody conjugation, tissue specimen preparation, multicolor QD staining, image processing and biomarker quantification. The results demonstrate that bioconjugated QDs can be used for multiplexed profiling of molecular biomarkers, and ultimately for correlation with disease progression and response to therapy. In general, QD bioconjugation is completed within 1 day, and multiplexed molecular profiling takes 1–3 days depending on the number of biomarkers and QD probes used.

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Bioconjugated quantum dots for multiplexed and quantitative immunohistochemistry

Cloud detection is one of the important tasks for remote sensing image processing. In this paper, a novel multilevel cloud detection method based on deep learning is proposed for remote sensing images. First, the simple linear iterative clustering (SLIC) method is improved to segment the image into good quality superpixels. Then, a deep convolutional neural network (CNN) with two branches is designed to extract the multiscale features from each superpixel and predict the superpixel as one of three classes including thick cloud, thin cloud, and noncloud. Finally, the predictions of all the superpixels in the image yield the cloud detection result. In the proposed cloud detection framework, the improved SLIC method can obtain accurate cloud boundaries by optimizing initial cluster centers, designing dynamic distance measure, and expanding search space. Moreover, different from traditional cloud detection methods that cannot achieve multilevel detection of cloud, the designed deep CNN model can not only detect cloud but also distinguish thin cloud from thick cloud. Experimental results indicate that the proposed method can detect cloud with higher accuracy and robustness than compared methods.

Multilevel Cloud Detection in Remote Sensing Images Based on Deep Learning

We construct a segmentation scheme that combines top-down with bottom-up processing. In the proposed scheme, segmentation and recognition are intertwined rather than proceeding in a serial manner. The top-down part applies stored knowledge about object shapes acquired through learning, whereas the bottom-up part creates a hierarchy of segmented regions based on uniformity criteria. Beginning with unsegmented training examples of class and non-class images, the algorithm constructs a bank of class-specific fragments and determines their figure-ground segmentation. This bank is then used to segment novel images in a top-down manner: the fragments are first used to recognize images containing class objects, and then to create a complete cover that best approximates these objects. The resulting segmentation is then integrated with bottom-up multi-scale grouping to better delineate the object boundaries. Our experiments, applied to a large set of four classes (horses, pedestrians, cars, faces), demonstrate segmentation results that surpass those achieved by previous top-down or bottom-up schemes. The main novel aspects of this work are the fragment learning phase, which efficiently learns the figure-ground labeling of segmentation fragments, even in training sets with high object and background variability; combining the top-down segmentation with bottom-up criteria to draw on their relative merits; and the use of segmentation to improve recognition.

Combined Top-Down/Bottom-Up Segmentation

This paper presents a novel, fast and semi-automatic method for accurate cell cluster segmentation and cell counting of digital tissue image samples. In pathological conditions, complex cell clusters are a prominent feature in tissue samples. Segmentation of these clusters is a major challenge for development of an accurate cell counting methodology. We address the issue of cluster segmentation by following a three step process. The first step involves pre-processing required to obtain the appropriate nuclei cluster boundary image from the RGB tissue samples. The second step involves concavity detection at the edge of a cluster to find the points of overlap between two nuclei. The third step involves segmentation at these concavities by using an ellipse-fitting technique. Once the clusters are segmented, individual nuclei are counted to give the cell count. The method was tested on four different types of cancerous tissue samples and shows promising results with a low percentage error, high true positive rate and low false discovery rate.

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Automated cell counting and cluster segmentation using concavity detection and ellipse fitting techniques

The article explains how to query a database of flower patent images using both an example flower image and color names. This database consists of images that have been digitized from photographs submitted as a part of applications for flower patents to the US Patents and Trademark Office. This database must be queried by both example images and color name, so that both those checking new patent applications and those buying patents for cultivation can use it.

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Indexing flower patent images using domain knowledge

Segmentation of images is an important aspect of image recognition. While grayscale image segmentation has become quite a mature field, much less work has been done with regard to color image segmentation. Until recently, this was predominantly due to the lack of available computing power and color display hardware that is required to manipulate true color images (24-bit). TOday, it is not uncommon to find a standard desktop computer system with a true-color 24-bit display, at least 8 million bytes of memory, and 2 gigabytes of hard disk storage. Segmentation of color images is not as simple as segmenting each of the three RGB color components separately. The difficulty of using the RGB color space is that it doesn't closely model the psychological understanding of color. A better color model, which closely follows that of human visual perception is the hue, saturation, intensity model. This color model separates the color components in terms of chromatic and achromatic information. Strickland et al. was able to show the importance of color in the extraction of edge features form an image. His method enhances the edges that are detectable in the luminance image with information from the saturation image. Segmentation of both the saturation and intensity components is easily accomplished with any gray scale segmentation algorithm, since these spaces are linear. The modulus 2(pi) nature of the hue color component makes its segmentation difficult. For example, a hue of 0 and 2(pi) yields the same color tint. Instead of applying separate image segmentation to each of the hue, saturation, and intensity components, a better method is to segment the chromatic component separately from the intensity component because of the importance that the chromatic information plays in the segmentation of color images. This paper presents a method of using the gray scale K-means algorithm to segment 24-bit color images. Additionally, this paper will show the importance the hue component plays in the segmentation of color images.

Color segmentation in the HSI color space using the K-means algorithm

Histogram equalization is a well known tool for enhancing the contrast and brightness of grayscale images. Grayscale histogram equalization has been extended to color images with limited success. One common method is to equalize the illumination component, while leaving the saturation and hue components unchanged. This method doesn't improve the overall color saturation of the image. Another approach is to apply equalization techniques in the RGB color space. The difficulty in using the RGB color space is that it does not correspond to human interpretation of color. This paper describes a method for histogram equalization of the saturation component using the color difference (or C-Y) color space. Since equalization of the saturation component alone leads to color artifacts, attention is given to the relationship that exists between saturation and intensity.

G. Eric Hague

Papers

Color segmentation in the HSI color space using the K-means algorithm

Histogram equalization of the saturation component for true-color images using the C-Y color space