• Journal of Korea Multimedia Society
• /
• v.10 no.7
• /
• pp.846-855
• /
• 2007

Significant feature extraction in cancer cell image analysis is an important process for grading cell carcinoma. In this study, we propose a method for 3D quantitative analysis of cell nuclei based upon digital image cytometry. First, we acquired volumetric renal cell carcinoma data for each grade using confocal laser scanning microscopy and segmented cell nuclei employing color features based upon a supervised teaming scheme. For 3D visualization, we used a contour-based method for surface rendering and a 3D texture mapping method for volume rendering. We then defined and extracted the 3D morphological features of cell nuclei. To evaluate what quantitative features of 3D analysis could contribute to diagnostic information, we analyzed the statistical significance of the extracted 3D features in each grade using an analysis of variance (ANOVA). Finally, we compared the 2D with the 3D features of cell nuclei and analyzed the correlations between them. We found statistically significant correlations between nuclear grade and 3D morphological features. The proposed method has potential for use as fundamental research in developing a new nuclear grading system for accurate diagnosis and prediction of prognosis.

• Journal of Korea Multimedia Society
• /
• v.24 no.8
• /
• pp.1000-1011
• /
• 2021

In recent years, using Deep Learning methods to apply for medical and biomedical image analysis has seen many advancements. In clinical, using Deep Learning-based approaches for cancer image analysis is one of the key applications for cancer detection and treatment. However, the scarcity and shortage of labeling images make the task of cancer detection and analysis difficult to reach high accuracy. In 2015, the Unet model was introduced and gained much attention from researchers in the field. The success of Unet model is the ability to produce high accuracy with very few input images. Since the development of Unet, there are many variants and modifications of Unet related architecture. This paper proposes a new approach of using Unet++ with pretrained EfficientNet as backbone architecture for breast tumor cell nuclei segmentation and uses the multi-organ transfer learning approach to segment nuclei of breast tumor cells. We attempt to experiment and evaluate the performance of the network on the MonuSeg training dataset and Triple Negative Breast Cancer (TNBC) testing dataset, both are Hematoxylin and Eosin (H & E)-stained images. The results have shown that EfficientUnet++ architecture and the multi-organ transfer learning approach had outperformed other techniques and produced notable accuracy for breast tumor cell nuclei segmentation.

• Journal of Biomedical Engineering Research
• /
• v.27 no.3
• /
• pp.125-130
• /
• 2006

The number of the living endothelial cells and the shape of those are very import clinical parameters for the evaluation of the quality of cornea. In this paper, we developed the automated endothelial cell counting and shape analysis algorithm for a confocal microscope. Since, the endothelial images from the confocal microscope has a non-uniform illumination and low contrast between cell boundaries and cell bodies, it is very difficult to segment the cells from the endothelial images. To cope with these difficulties, we proposed the new two stage image processing algorithm. At first stage algorithm, we used a high-pass filter and histogram equalization to compensate the non-uniform brightness pattern and a morphological filter and a watershed method are applied to detect the boundary of cells. From this stage, we could count the number of cells in an endothelial image. At second stage algorithm, we used a Voronoi diagram method to classify the shape of cells. This cell shape analysis and the percent of hexagonal cells are very sensitive in detecting the early endothelium damage. To evaluate the performance of the proposed system, we p개cessed seven endothelial images obtained using a confocal microscope. The proposed system correctly counted 95.5% cells and classified 92.0% of hexagonal cell shapes. This result is better than any others in this research area.

• Journal of Internet Computing and Services
• /
• v.16 no.1
• /
• pp.39-46
• /
• 2015

This paper presents an automatic method to detect and count the cells from microorganism images based on mobile environments. Cell counting is an important process in the field of biological and pathological image analysis. In the past, cell counting is done manually, which is known as tedious and time consuming process. Moreover, the manual cell counting can lead inconsistent and imprecise results. Therefore, it is necessary to make an automatic method to detect and count cells from biological images to obtain accurate and consistent results. The proposed multi-step cell counting method automatically segments the cells from the image of cultivated microorganism and labels the cells by utilizing topological analysis of the segmented cells. To improve the accuracy of the cell counting, we adopt watershed algorithm in separating agglomerated cells from each other and morphological operation in enhancing the individual cell object from the image. The system is developed by considering the availability in mobile environments. Therefore, the cell images can be obtained by a mobile phone and the processed statistical data of microorganism can be delivered by mobile devices in ubiquitous smart space. From the experiments, by comparing the results between manual and the proposed automatic cell counting we can prove the efficiency of the developed system.

• Mass Spectrometry Letters
• /
• v.14 no.4
• /
• pp.121-140
• /
• 2023

It is becoming more and more clear that each cell, even those of the same type, has a unique identity. This sophistication and the diversity of cell types in tissue are what are pushing the necessity for spatially distributed omics at the single-cell (SC) level. Single-cell chemical assessment, which also provides considerable insight into biological, clinical, pharmacodynamic, pathological, and toxicity studies, is crucial to the investigation of cellular omics (genomics, metabolomics, etc.). Mass spectrometry (MS) as a tool to image and profile single cells and subcellular organelles facilitates novel technical expertise for biochemical and biomedical research, such as assessing the intracellular distribution of drugs and the biochemical diversity of cellular populations. It has been illustrated that ambient mass spectrometry (AMS) is a valuable tool for the rapid, straightforward, and simple analysis of cellular and sub-cellular constituents and metabolites in their native state. This short review examines the advances in ambient mass spectrometry (AMS) and ambient mass spectrometry imaging (AMSI) on single-cell analysis that have been authored in recent years. The discussion also touches on typical single-cell AMS assessments and implementations.

• Journal of the Korea Society of Computer and Information
• /
• v.24 no.11
• /
• pp.71-78
• /
• 2019

Most 3D displays that are currently in the market adopt the binocular disparity method creating a different image for the left and right eye for a 3 dimensional effect. However, commercialized 3D image output devices lack in performance making it uncomfortable for the viewer and restrict the viewer to certain positions. In this paper, we propose a single-cell polarized lens type stereoscopic system which has a smaller viewing angle and reduced crosstalk, with improved light penetration compared to existing double-cell structures; and analyzed the single-cell polarized lens type stereoscopic system properties, and conducted an effect analysis of performance improvement compared to the dual-cell type. Results showed that the single-cell type had a 25% improved performance, and the 3D crosstalk index which is an important index for quality characteristics of stereoscopic systems, increased over about 37%, compared to the dual-cell type.

• International Journal of Control, Automation, and Systems
• /
• v.4 no.5
• /
• pp.637-644
• /
• 2006

Biomedical image is often complex. An applied image analysis system should deal with the images which are of quite low quality and are challenging to segment. This paper presents a framework for color cell image segmentation by learning and classification online. It is a robust two-stage scheme using kernel method and watershed transform. In first stage, a two-class SVM is employed to discriminate the pixels of object from background; where the SVM is trained on the data which has been analyzed using the mean shift procedure. A real-time training strategy is also developed for SVM. In second stage, as the post-processing, local watershed transform is used to separate clustering cells. Comparison with the SSF (Scale space filter) and classical watershed-based algorithm (those are often employed for cell image segmentation) is given. Experimental results demonstrate that the new method is more accurate and robust than compared methods.

• Journal of the Institute of Convergence Signal Processing
• /
• v.12 no.2
• /
• pp.83-90
• /
• 2011

In cell biology area, microscopy enables detecting objects inside cells that are stained or fluorescently tagged. It is disadvantageous for observing these objects because of the noisy characteristics of their environmental surrounding. In this paper, a framework is proposed to increase the throughput and reliability for analysis of these images. First, we apply adaptive beamlet transform to extract edges meaningfully followed by orientation, location, and length in different scales. Then, a post-process is implemented to extend and map them onto original image. Our proposed scheme is compared with Canny edge detector and conventional beamlet transform from four evaluation aspects. It produces better results when experiments are conducted on real images. Much better results for observing internal parts make this framework competitive for analysis of cell images.

• Journal of Information Processing Systems
• /
• v.16 no.1
• /
• pp.113-119
• /
• 2020

This study proposed the structure of the cluster's cell counting algorithm for cell analysis. The image required for cell count is taken under a microscope. At present, the cell counting algorithm is reported to have a problem of low accuracy of results due to uneven shape and size clusters. To solve these problems, the proposed algorithm has a feature of calculating the number of cells in a cluster by applying a radius change analysis to the existing distance conversion and watershed algorithm. Later, cell counting algorithms are expected to yield reliable results if applied to the required field.

• Journal of the Korea Society of Computer and Information
• /
• v.4 no.1
• /
• pp.47-53
• /
• 1999

In medical field, the computer has been used in the automatic processing of data derived in hospital. the automation of diagonal devices, and processing of medical digital images. In this paper, we classify red blood cell into 16 class including normal cell to the automation of blood analysis to diagnose disease. First, using UNL Fourier and invariant moment algorithm, we extract features of red blood cell from blood cell image and then construct multi-layer backpropagation neural network to recognize. We proof that the system can give support to blood analyzer through blood sample analysis of 10 patients.