IU Indianapolis ScholarWorks :: Browsing by Subject "image segmentation"

Browsing by Subject "image segmentation"

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Boundary Segmentation For Fluorescence Microscopy Using Steerable Filters
(SPIE, 2017) Ho, David Joon; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
Fluorescence microscopy is used to image multiple subcellular structures in living cells which are not readily observed using conventional optical microscopy. Moreover, two-photon microscopy is widely used to image structures deeper in tissue. Recent advancement in fluorescence microscopy has enabled the generation of large data sets of images at different depths, times, and spectral channels. Thus, automatic object segmentation is necessary since manual segmentation would be inefficient and biased. However, automatic segmentation is still a challenging problem as regions of interest may not have well defined boundaries as well as non-uniform pixel intensities. This paper describes a method for segmenting tubular structures in fluorescence microscopy images of rat kidney and liver samples using adaptive histogram equalization, foreground/background segmentation, steerable filters to capture directional tendencies, and connected-component analysis. The results from several data sets demonstrate that our method can segment tubular boundaries successfully. Moreover, our method has better performance when compared to other popular image segmentation methods when using ground truth data obtained via manual segmentation.
Building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans using landmark-free surface registration
(IEEE, 2016-10) Cong, Shan; Rizkalla, Maher; Salama, Paul; Electrical and Computer Engineering, School of Engineering and Technology
The hippocampus is widely studied in neuroimaging field as it plays important roles in memory and learning. However, the critical subfield information is often not explored in most hippocampal studies. We previously proposed a method for hippocampal subfield morphometry by integrating FreeSurfer, FSL, and SPHARM tools. But this method had some limitations, including the analysis of T1-weighted MRI scans without detailed subfield information and hippocampal registration without using important subfield information. To bridge these gaps, in this work, we propose a new framework for building a surface atlas of hippocampal subfields from high resolution T2-weighted MRI scans by integrating state-of-the-art methods for automated segmentation of hippocampal subfields and landmark-free, subfield-aware registration of hippocampal surfaces. Our experimental results have shown the promise of the new framework.
Clustering Algorithm Based Straight and Curved Crop Row Detection Using Color Based Segmentation
(ASME, 2021-02) Khan, Nazmuzzaman; Rajendran, Veera P.; Al Hasan, Mohammad; Anwar, Sohel; Mechanical and Energy Engineering, School of Engineering and Technology
Autonomous navigation of agricultural robot is an essential task in precision agriculture, and success of this task critically depends on accurate detection of crop rows using computer vision methodologies. This is a challenging task due to substantial natural variations in crop row images due to various factors, including, missing crops in parts of a row, high and irregular weed growth between rows, different crop growth stages, different inter-crop spacing, variation in weather condition, and lighting. The processing time of the detection algorithm also needs to be small so that the desired number of image frames from continuous video can be processed in real-time. To cope with all the above mentioned requirements, we propose a crop row detection algorithm consisting of the following three linked stages: (1) color based segmentation for differentiating crop and weed from background, (2) differentiating crop and weed pixels using clustering algorithm and (3) robust line fitting to detect crop rows. We test the proposed algorithm over a wide variety of scenarios and compare its performance against four different types of existing strategies for crop row detection. Experimental results show that the proposed algorithm perform better than the competing algorithms with reasonable accuracy. We also perform additional experiment to test the robustness of the proposed algorithm over different values of the tuning parameters and over different clustering methods, such as, KMeans, MeanShift, Agglomerative, and HDBSCAN.
Nuclei Segmentation of Fluorescence Microscopy Images Using Three Dimensional Convolutional Neural Networks
(IEEE, 2017-07) Ho, David Joon; Fu, Chichen; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
Fluorescence microscopy enables one to visualize subcellular structures of living tissue or cells in three dimensions. This is especially true for two-photon microscopy using near-infrared light which can image deeper into tissue. To characterize and analyze biological structures, nuclei segmentation is a prerequisite step. Due to the complexity and size of the image data sets, manual segmentation is prohibitive. This paper describes a fully 3D nuclei segmentation method using three dimensional convolutional neural networks. To train the network, synthetic volumes with corresponding labeled volumes are automatically generated. Our results from multiple data sets demonstrate that our method can successfully segment nuclei in 3D.
Robust Weed Recognition Through Color Based Image Segmentation and Convolution Neural Network Based Classification
(ASME, 2019-11) Khan, M. Nazmuzzaman; Anwar, Sohel; Mechanical and Energy Engineering, School of Engineering and Technology
Current image classification techniques for weed detection (classic vision techniques and deep-neural net) provide encouraging results under controlled environment. But most of the algorithms are not robust enough for real-world application. Different lighting conditions and shadows directly impact vegetation color. Varying outdoor lighting conditions create different colors, noise levels, contrast and brightness. High component of illumination causes sensor (industrial camera) saturation. As a result, threshold-based classification algorithms usually fail. To overcome this shortfall, we used visible spectral-index based segmentation to segment the weeds from background. Mean, variance, kurtosis, and skewness are calculated for each input image and image quality (good or bad) is determined. Bad quality image is converted to good-quality image using contrast limited adaptive histogram equalization (CLAHE) before segmentation. A convolution neural network (CNN) based classifier is then trained to classify three different types of weed (Ragweed, Pigweed and Cocklebur) common in a corn field. The main objective of this work is to construct a robust classifier, capable of classifying between three weed species in the presence of occlusion, noise, illumination variation, and motion blurring. Proposed histogram statistics-based image enhancement process solved weed mis-segmentation under extreme lighting condition. CNN based classifier shows accurate, robust classification under low-to-mid level motion blurring and various levels of noise.
Segmentation of fluorescence microscopy images using three dimensional active contours with inhomogeneity correction
(IEEE, 2017-04) Lee, Soonam; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
Image segmentation is an important step in the quantitative analysis of fluorescence microscopy data. Since fluorescence microscopy volumes suffer from intensity inhomogeneity, low image contrast and limited depth resolution, poor edge details, and irregular structure shape, segmentation still remains a challenging problem. This paper describes a nuclei segmentation method for fluorescence microscopy based on the use of three dimensional (3D) active contours with inhomogeneity correction. The correction information utilizes 3D volume information while addressing intensity inhomogeneity across vertical and horizontal directions. Experimental results demonstrate that the proposed method achieves better performance than other reported methods.
Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation
(IEEE, 2018-06) Fu, Chichen; Lee, Soonam; Ho, David Joon; Han, Shuo; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue. Segmentation is a required step to characterize and analyze biological structures in the images and recent 3D segmentation using deep learning has achieved promising results. One issue is that deep learning techniques require a large set of groundtruth data which is impractical to annotate manually for large 3D microscopy volumes. This paper describes a 3D deep learning nuclei segmentation method using synthetic 3D volumes for training. A set of synthetic volumes and the corresponding groundtruth are generated using spatially constrained cycle-consistent adversarial networks. Segmentation results demonstrate that our proposed method is capable of segmenting nuclei successfully for various data sets.
Tubule Segmentation of Fluorescence Microscopy Images Based on Convolutional Neural Networks With Inhomogeneity Correction
(Society for Imaging Science and Technology, 2018) Lee, Soonam; Fu, Chichen; Salama, Paul; Dunn, Kenneth W.; Delp, Edward J.; Electrical and Computer Engineering, School of Engineering and Technology
Fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells. However, quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering. Moreover, manual quantification of such image volumes is an intractable and error-prone process, making the need for automated image analysis methods crucial. This paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction. The segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods. Experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods.
Using Flow Feature to Extract Pulsatile Blood Flow from 4D Flow MRI Images
(International Society for Optics and Photonics, 2017-02) Wang, Zhiqiang; Zhao, Ye; Yu, Whitney; Chen, Xi; Lin, Chen; Kralik, Stephen F.; Hutchins, Gary D.; Surgery, School of Medicine
4D flow MRI images make it possible to measure pulsatile blood flow inside deforming vessel, which is critical in accurate blood flow visualization, simulation, and evaluation. Such data has great potential to overcome problems in existing work, which usually does not reflect the dynamic nature of elastic vessels and blood flows in cardiac cycles. However, the 4D flow MRI data is often low-resolution and with strong noise. Due to these challenges, few eﬀorts have been successfully conducted to extract dynamic blood flow fields and deforming artery over cardiac cycles, especially for small artery like carotid. In this paper, a robust flow feature, particularly the mean flow intensity is used to segment blood flow regions inside vessels from 4D flow MRI images in whole cardiac cycle. To estimate this flow feature more accurately, adaptive weights are added to the raw velocity vectors based on the noise strength of MRI imaging. Then, based on this feature, target arteries are tracked in at diﬀerent time steps in a cardiac cycle. This method is applied to the clinical 4D flow MRI data in neck area. Dynamic vessel walls and blood flows are eﬀectively generated in a cardiac cycle in the relatively small carotid arteries. Good image segmentation results on 2D slices are presented, together with the visualization of 3D arteries and blood flows. Evaluation of the method was performed by clinical doctors and by checking flow volume rates in the vertebral and carotid arteries.
Using Flow Feature to Extract Pulsatile Blood Flow from 4D Flow MRI Images
(SPIE, 2017) Wang, Zhiqiang; Zhao, Ye; Yu, Whitney; Chen, Xi; Lin, Chen; Kralik, Stephen F.; Hutchins, Gary D.; Mechanical Engineering, School of Engineering and Technology
4D flow MRI images make it possible to measure pulsatile blood flow inside deforming vessel, which is critical in accurate blood flow visualization, simulation, and evaluation. Such data has great potential to overcome problems in existing work, which usually does not reflect the dynamic nature of elastic vessels and blood flows in cardiac cycles. However, the 4D flow MRI data is often low-resolution and with strong noise. Due to these challenges, few eﬀorts have been successfully conducted to extract dynamic blood flow fields and deforming artery over cardiac cycles, especially for small artery like carotid. In this paper, a robust flow feature, particularly the mean flow intensity is used to segment blood flow regions inside vessels from 4D flow MRI images in whole cardiac cycle. To estimate this flow feature more accurately, adaptive weights are added to the raw velocity vectors based on the noise strength of MRI imaging. Then, based on this feature, target arteries are tracked in at diﬀerent time steps in a cardiac cycle. This method is applied to the clinical 4D flow MRI data in neck area. Dynamic vessel walls and blood flows are eﬀectively generated in a cardiac cycle in the relatively small carotid arteries. Good image segmentation results on 2D slices are presented, together with the visualization of 3D arteries and blood flows. Evaluation of the method was performed by clinical doctors and by checking flow volume rates in the vertebral and carotid arteries.

Browsing by Subject "image segmentation"

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