UC Riverside

Images play an increasingly important role in many fields of science and its applications. Biology is one of the best examples of fields that have come to depend heavily upon images for progress. Biological images contain a lot of objects and patterns, which may contain information about underlying mechanism in biology. Image analysis provides a way to extract and quantify objects and patterns in image data and obtain answers to meaningful biological questions. This dissertation focuses on image analysis for biological applications in segmentation, registration and count estimation problems.

Proper understanding of the causal relationship between cell growth patterns and gene expression dynamics is one of the major topics of interest in developmental biology. Information such as rates and patterns of cell expansion play a critical role in explaining cell growth and deformation dynamics. In our research, we focus on studying the developing plant with the goal of obtaining very accurate cell development statistics. The image processing and analysis framework for gathering the cell growth and division statistics comprises of three main parts - image registration, cell segmentation and cell tracking. Without proper segmentation and registration the subsequent parts in the image analysis system would fail. This dissertation addresses both these problems.

To provide proper segmentation of cells we propose a single framework that entails segmentation and tracking of plant cell images. We show how to optimally choose the parameters in the watershed segmentation algorithm for high quality segmentation results. To register image datasets we have provided an optimization based framework to select the best image slice correspondence from consecutive image stacks by using the tissue characteristics in images. Also, we have presented a novel landmark selection method where we use characteristics of neighboring cells as unique features. To evaluate both our frameworks, cell correspondences across multiple slices and

time windows are fused to obtain cell lineages and compared to recent results in this area. Experiments on multiple plant datasets show the proposed algorithms provide significantly longer, more accurate cell lineages and more comprehensive identification of cell divisions.

Another contribution of this work is in count estimation. Mosquitoes

and other blood-feeding insects transmit deadly diseases to hundreds of millions of people, causing severe suffering and more than a million deaths each year. Female mosquitoes that transmit deadly diseases locate human hosts by detecting exhaled CO2 and skin odor. Quantitative analysis of a mosquito’s attraction to different odors is very important. It can lead to discoveries about mosquitoes that could have a big impact on human health. We present a method to automate mosquito counting in videos. The current manual analysis on these videos is not sufficient for quantitative analysis and available object counting approaches do not work well. We propose an automated mosquito counting technique which uses the softmax classifier with a sliding window

technique to detect the mosquitoes in the separated region of interest, where the over-detections resulting from the sliding window technique are eliminated by non-maximum suppression method. The final counts of the mosquito detections represent the desired mosquito counts in images. The proposed automated method has been applied on different datasets and showed very good and consistent counts of mosquitoes.