102 research outputs found
Polar Angle Detection and Image Combination Based Leukocyte Segmentation for Overlapping Cell Images
Leukocyte segmentation is one of the essential steps in an automatic leukocyte recognition system. Due to the complexity of the overlapping cell images, methods for leukocyte segmentation are still needed. In this paper, we first construct a combined image by saturation and green channels to extract the nucleus and in turn locate a cursory circular region of the leukocyte. Then the boundary of the leukocyte is represented by the polar coordinate. We determine the overlapping area by polar angle detection. Finally, another combined image is built based on the red and blue channels of the sub image covering the overlap to segment the leukocyte. The paper reports a promising segmentation for 60 microscopic cell images
Automated Quantification of White Blood Cells in Light Microscopic Images of Injured Skeletal Muscle
Muscle regeneration process tracking and analysis aim to monitor the injured muscle tissue section over time and analyze the muscle healing procedure. In this procedure, as one of the most diverse cell types observed, white blood cells (WBCs) exhibit dynamic cellular response and undergo multiple protein expression changes. The characteristics, amount, location, and distribution compose the action of cells which may change over time. Their actions and relationships over the whole healing procedure can be analyzed by processing the microscopic images taken at different time points after injury. The previous studies of muscle regeneration usually employ manual approach or basic intensity process to detect and count WBCs. In comparison, computer vision method is more promising in accuracy, processing speed, and labor cost. Besides, it can extract features like cell/cluster size and eccentricity fast and accurately.
In this thesis, we propose an automated quantifying and analysis framework to analyze the WBC in light microscope images of uninjured and injured skeletal muscles. The proposed framework features a hybrid image segmentation method combining the Localized Iterative Otsu’s threshold method assisted by neural networks classifiers and muscle edge detection. In specific, both neural network and convoluted neural network based classifiers are studied and compared. Via this framework, the CD68-positive WBC and 7/4-positive WBC quantification and density distribution results are analyzed for demonstrating the effectiveness of the proposed method
A color and shape based algorithm for segmentation of white blood cells in peripheral blood and bone marrow images
Cataloged from PDF version of article.Computer-based imaging systems are becoming important tools for quantitative assessment
of peripheral blood and bone marrow samples to help experts diagnose blood disorders
such as acute leukemia. These systems generally initiate a segmentation stage
where white blood cells are separated from the background and other nonsalient objects.
As the success of such imaging systems mainly depends on the accuracy of this stage,
studies attach great importance for developing accurate segmentation algorithms.
Although previous studies give promising results for segmentation of sparsely distributed
normal white blood cells, only a few of them focus on segmenting touching and overlapping
cell clusters, which is usually the case when leukemic cells are present. In this article,
we present a new algorithm for segmentation of both normal and leukemic cells in
peripheral blood and bone marrow images. In this algorithm, we propose to model color
and shape characteristics of white blood cells by defining two transformations and introduce
an efficient use of these transformations in a marker-controlled watershed algorithm.
Particularly, these domain specific characteristics are used to identify markers and
define the marking function of the watershed algorithm as well as to eliminate false white
blood cells in a postprocessing step. Working on 650 white blood cells in peripheral
blood and bone marrow images, our experiments reveal that the proposed algorithm
improves the segmentation performance compared with its counterparts, leading to high accuracies for both sparsely distributed normal white blood cells and dense leukemic cell clusters. (C) 2014 International Society for Advancement of Cytometr
A fully automated end-to-end process for fluorescence microscopy images of yeast cells:From segmentation to detection and classification
In recent years, an enormous amount of fluorescence microscopy images were
collected in high-throughput lab settings. Analyzing and extracting relevant
information from all images in a short time is almost impossible. Detecting
tiny individual cell compartments is one of many challenges faced by
biologists. This paper aims at solving this problem by building an end-to-end
process that employs methods from the deep learning field to automatically
segment, detect and classify cell compartments of fluorescence microscopy
images of yeast cells. With this intention we used Mask R-CNN to automatically
segment and label a large amount of yeast cell data, and YOLOv4 to
automatically detect and classify individual yeast cell compartments from these
images. This fully automated end-to-end process is intended to be integrated
into an interactive e-Science server in the PerICo1 project, which can be used
by biologists with minimized human effort in training and operation to complete
their various classification tasks. In addition, we evaluated the detection and
classification performance of state-of-the-art YOLOv4 on data from the
NOP1pr-GFP-SWAT yeast-cell data library. Experimental results show that by
dividing original images into 4 quadrants YOLOv4 outputs good detection and
classification results with an F1-score of 98% in terms of accuracy and speed,
which is optimally suited for the native resolution of the microscope and
current GPU memory sizes. Although the application domain is optical microscopy
in yeast cells, the method is also applicable to multiple-cell images in
medical application
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