Sundaresh Ram

Segmentation and detection of fluorescent 3D spots

The 3D spatial organization of genes and other genetic elements within the nucleus is important for regulating gene expression. Understanding how this spatial organization is established and maintained throughout the life of a cell is key to elucidating the many layers of gene regulation. Quantitative methods for studying nuclear organization will lead to insights into the molecular mechanisms that maintain gene organization as well as serve as diagnostic tools for pathologies caused by loss of nuclear structure. However, biologists currently lack automated and high throughput methods for quantitative and qualitative global analysis of 3D gene organization. In this study, we use confocal microscopy and fluorescence in‐situ hybridization (FISH) as a cytogenetic technique to detect and localize the presence of specific DNA sequences in 3D. FISH uses probes that bind to specific targeted locations on the chromosomes, appearing as fluorescent spots in 3D images obtained using fluorescence microscopy. In this article, we propose an automated algorithm for segmentation and detection of 3D FISH spots. The algorithm is divided into two stages: spot segmentation and spot detection. Spot segmentation consists of 3D anisotropic smoothing to reduce the effect of noise, top‐hat filtering, and intensity thresholding, followed by 3D region‐growing. Spot detection uses a Bayesian classifier with spot features such as volume, average intensity, texture, and contrast to detect and classify the segmented spots aseither true or false spots. Quantitative assessment of the proposed algorithm demonstrates improved segmentation and detection accuracy compared to other techniques.

Single image super-resolution using dictionary-based local regression

This paper presents a new method of producing a high-resolution image from a single low-resolution image without any external training image sets. We use a dictionary-based regression model for practical image super-resolution using local self-similar example patches within the image. Our method is inspired by the observation that image patches can be well represented as a sparse linear combination of elements from a chosen over-complete dictionary and that a patch in the high-resolution image have good matches around its corresponding location in the low-resolution image. A first-order approximation of a nonlinear mapping function, learned using the local self-similar example patches, is applied to the low-resolution image patches to obtain the corresponding high-resolution image patches. We show that the proposed algorithm provides improved accuracy compared to the existing single image super-resolution methods by running them on various input images that contain diverse textures, and that are contaminated by noise or other artifacts.

Size-invariant cell nucleus segmentation in 3-D microscopy

Accurate segmentation of 3-D cell nuclei in microscopy images is an essential task in many biological studies. Traditional image segmentation methods are challenged by the complexity and variability of microscope images, so there is a need to improve segmentation accuracy and reliability, as well as the level of automation. In this paper we present a novel automated algorithm for robust segmentation of 3-D cell nuclei using a combination of ideas. Our algorithm includes the following steps: image denoising, binarization, seed detection using the fast radial symmetric transform (FRST), initial segmentation using the random walker algorithm and the 3-D watershed algorithm, and final refinements using 3-D active contours. We show that our algorithm provides improved accuracy compared to existing segmentation algorithms.

A comparison of tracking algorithm performance for objects in wide area imagery

Object tracking is currently one of the most active research areas in computer vision. In this paper we compare and analyze the performance of six recent object tracking algorithms on a raw, low resolution, unregistered, interlaced aerial video of multiple cars moving on a roadway. This dataset comprising 50 frames of video offers a wide variety of challenges related to imaging issues such as low resolution, unregistered frames, camera motion, and interlaced video, as well as object detection problems such as low contrast, background clutter, object occlusion and varying degrees of motion. We present the performance of these algorithms in terms of both overlap accuracy and the Euclidean distance of the center pixel returned by the tracking algorithm from the ground truth.

Size-Invariant Detection of Cell Nuclei in Microscopy Images

Accurate detection of individual cell nuclei in microscopy images is an essential and fundamental task for many biological studies. In particular, multivariate fluorescence microscopy is used to observe different aspects of cells in cultures. Manual detection of individual cell nuclei by visual inspection is time consuming, and prone to induce subjective bias. This makes automatic detection of cell nuclei essential for large-scale, objective studies of cell cultures. Blur, clutter, bleed-through, imaging noise and touching and partially overlapping nuclei with varying sizes and shapes make automated detection of individual cell nuclei a challenging task using image analysis. In this paper we propose a new automated method for fast and robust detection of individual cell nuclei based on their radial symmetric nature in fluorescence in-situ hybridization (FISH) images obtained via confocal microscopy. The main contributions are two-fold. 1) This work presents a more accurate cell nucleus detection system using the fast radial symmetry transform (FRST). 2) The proposed cell nucleus detection system is robust against most occlusions and variations in size and moderate shape deformations. We evaluate the performance of the proposed algorithm using precision/recall rates, Fβ-score and root-mean-squared distance (RMSD) and show that our algorithm provides improved detection accuracy compared to existing algorithms.

Symmetry-based detection of nuclei in microscopy images

Accurate detection of individual cell nuclei in microscopic images is an essential task for many biological studies. Blur, clutter, bleed through, imaging noise and touching and partially overlapping nuclei with varying sizes and shapes make automated detection of individual cell nuclei a challenging task using image analysis. In this paper we propose an automated method for robust detection of individual cell nuclei in fluorescence in-situ hybridization (FISH) images obtained via confocal microscopy. Our algorithm consists of the following steps: image denoising, binarization, detection of nuclear seed points combining the fast radial symmetric transform (FRST) and a distance-based non-maximum suppression. We show that our algorithm provides improved detection accuracy compared to the existing algorithms.

Image super-resolution using graph regularized block sparse representation

Recently, patch-based sparse representation has been used as a statistical image modeling technique for various image restoration applications, due to its ability to model well the natural image patches and automatically discover interpretable visual patterns. Standard sparse representation however does not consider the intrinsic and geometric structure present in the data, thereby leading to sub-optimal results. In this paper, we exploit the concept that a signal is block sparse in a given basis-i.e., the non-zero elements occur in clusters of varying sizes-and propose an efficient framework for learning sparse representation modeling of natural images, called graph regularized block sparse representation (GRBSR). The proposed GRBSR is able to sparsely represent natural images in the domain of a block, which enforces the intrinsic local sparsity. We apply the proposed GRBSR to learn a dictionary-based local regression model for super-resolving a single low-resolution image without any external training image sets. We show that the proposed method provides improved performance as compared to the existing single-image super-resolution methods by running them on various input images containing diverse textures or other artifacts.

Superpixels using morphology for rock image segmentation

Detection and segmentation of rocks is an important first task in many applications such as geological analysis, planetary science and mining processes. Rocks are usually segmented using a variety of features such as texture, shading, shape and edges. It is easier to compute these features for rock superpixels rather than every pixel in the image. A superpixel is a group of spatially coherent pixels that form a meaningful homogeneous region, usually belonging to the same object. In this paper, we perform a comparative study of some of the current superpixel algorithms on rock images with regard to their ability to adhere to image boundaries, their speed, and their impact on rock segmentation performance. Also, we propose a new and very simple superpixel algorithm, Superpixels Using Morphology (SUM), which permutes a watershed transformation approach to efficiently generate superpixels. We show that SUM achieves a performance comparable to the recent superpixel algorithms on the rock images.

Random walker watersheds: A new image segmentation approach

We propose a new graph-based approach for performing a multilabel, interactive image segmentation using the principle of random walks. Using the random walk principle, given a set of user-defined (or prelabeled) pixels as labels, one can analytically calculate the probability of walking from each unlabeled pixel to each labeled pixel, thereby defining a vector of probabilities for each unlabeled pixel. By efficiently combining this vector of probabilities obtained for each unlabeled pixel, they can be assigned to one of the labels using the watershed algorithm to obtain an image segmentation. We present quantitative and qualitative results, comparing our new algorithm with the original random walker image segmentation algorithm.

Three-Dimensional Segmentation of the Ex-Vivo Anterior Lamina Cribrosa From Second-Harmonic Imaging Microscopy

The lamina cribrosa (LC) is a connective tissue in the posterior eye with a complex mesh-like trabecular microstructure, through which all the retinal ganglion cell axons and central retinal vessels pass. Recent studies have demonstrated that changes in the structure of the LC correlate with glaucomatous damage. Thus, accurate segmentation and reconstruction of the LC is of utmost importance. This paper presents a new automated method for segmenting the microstructure of the anterior LC in the images obtained via multiphoton microscopy using a combination of ideas. In order to reduce noise, we first smooth the input image using a 4-D collaborative filtering scheme. Next, we enhance the beam-like trabecular microstructure of the LC using wavelet multiresolution analysis. The enhanced LC microstructure is then automatically extracted using a combination of histogram thresholding and graph-cut binarization. Finally, we use morphological area opening as a postprocessing step to remove the small and unconnected 3-D regions in the binarized images. The performance of the proposed method is evaluated using mutual overlap accuracy, Tanimoto index, F-score, and Rand index. Quantitative and qualitative results show that the proposed algorithm provides improved segmentation accuracy and computational efficiency compared to the other recent algorithms.