Analysis of blood and bone marrow smears using digital image processing techniques

Hengen, Heiko; Spoor, Susanne L.; Pandit, Μ.

doi:10.1117/12.467205

Cited by 31 publications

(21 citation statements)

References 2 publications

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“…In the past decades, there are only several groups working in it. Among them, both Pandit [14,15,16] and Keller's group [17,18,19,20,21] did a lot of meaningful work in the segmentation, feature extraction, and classification of the white blood cells in the grey or color bone marrow image. In the analysis procedures, the most important and difficult part is segmentation, but for a practical system, the reported segmentation results still need enhancement.…”

Section: Bone Marrow Smear Images Analysismentioning

confidence: 99%

Analysis of blood and bone marrow smears using multispectral imaging analysis techniques

Zeng

et al. 2005

SPIE Proceedings

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Counting of different classes of white blood cells in bone marrow smears can give pathologists valuable information regarding various cancers. But it is tedious to manually locate, identify, and count these classes of cells, even by skilled hands. This paper presents a novel approach for automatic detection of White Blood Cells in bone marrow microscopic images. Different from traditional color imaging method, we use multispectral imaging techniques for image acquisition. The combination of conventional digital imaging with spectroscopy can provide us with additional useful spectral information in common pathological samples. With our spectral calibration method, device-independent images can be acquired, which is almost impossible in conventional color imaging method. A novel segmentation algorithm using spectral operation is presented in this paper. Experiments show that the segmentation is robust, precise, with low computational cost and insensitive to smear staining and illumination condition. Once the nuclei and cytoplasm have been segmented, more than a hundred of features are extracted under the direction of a pathologist, including shape features, textural features and spectral ratio features. In pattern recognition, a maximum likelihood classifier(MLC) is implemented in a hierarchical tree. The classification results are also discussed. This paper is focused on image acquisition and segmentation.

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Section: Bone Marrow Smear Images Analysismentioning

confidence: 99%

Analysis of blood and bone marrow smears using multispectral imaging analysis techniques

Zeng

et al. 2005

SPIE Proceedings

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“…(4) A watershed algorithm is implemented to S 2 for declustering of the inevitable clusters[please refer to documents [18,19]]. And then delete small particles, and then mask S 3 is formed.…”

Section: Nucleus Segmentationmentioning

confidence: 99%

A multispectral imaging analysis system for early detection of cervical cancer

Zeng

et al. 2005

Medical Imaging 2005: Physics of Medical Imaging

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Cervical cancer is the second most common cancer among women worldwide. Early detection of cervical cancer is very important for successful treatment and increasing survival. Papanicolaou test is the most popular and effective screening test for cervical cancer, but it is highly subjective and skilled-labor intensive. We report a multispectral imaging microscopic system for Papanicolaou smear analysis for early detection of cervical cancer. Different from traditional color imaging method, we use multispectral imaging techniques for image acquisition, which can simultaneously record spectral and spatial information of a sample. A liquid crystal tunable filter (LCTF) is coupled in the light microscope for fast wavelength selection and a two-dimensional cooled charge-coupled device (CCD) for image capture. In this paper, the multispectral image acquisition method is introduced, including exposure control and spectral calibration, which makes the images not so dependent on imaging devices. In the image segmentation process, an effective algorithm using spectral ratio method is applied for cell nuclei detection. This segmentation method can easily detect the nuclei and diminish the influence of the cytoplasm overlap. Results show that our segmentation is more robust and precise than conventional color imaging method which is heavily dependant on sample staining and illumination conditions while with high speed. Once the nuclei have been segmented, cell features including morphological and textural features are measured. A genetic algorithm is used for feature selection and a support vector machine(SVM) is used for training and classification. This paper is focused on image acquisition and segmentation.

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“…Cells splitting can be used to overcome the problem of overlapping using automatic or manual techniques [14,[21][22]. Automatic cell splitting is applied using watershed transform followed by morphological operator to overcome overlapping and cell splitting.…”

Section: International Journal Of Computer Applications (0975 -8887) mentioning

confidence: 99%

“…Hengen H and Labati RD. [22] retained the shape by eroding and region growing to separate the clumped cells. They showed that automated cells splitting by watershed technique is better for cell counting.…”

Section: Introductionmentioning

confidence: 99%

Automatic Segmentation of Acute Leukemia Cells

Kandil¹,

Hassan²

2016

IJCA

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The recognition of the acute Leukemia blast cells in colored microscopic images is a challenging task. Segmentation is the essential step for image analysis and image processing. In this paper, an algorithm is presented that consists of panel selection followed by segmentation using K-means clustering then a refinement process. This algorithm was applied on public dataset designed for testing segmentation techniques. The results were compared with two different segmentation techniques developed by other researchers on the same data set. Our algorithm results in a sensitivity of 97.4 % and specificity of 98.1%. The developed algorithm was tested to another dataset of samples extracted from patients in local hospitals. The algorithm results in sensitivity of 100%, Specificity of 99.747% and accuracy of 99.7617%. The results were approved by expert pathologists. General TermsPattern Recognition, Image processing and segmentation.

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Analysis of blood and bone marrow smears using digital image processing techniques

Cited by 31 publications

References 2 publications

Analysis of blood and bone marrow smears using multispectral imaging analysis techniques

Analysis of blood and bone marrow smears using multispectral imaging analysis techniques

A multispectral imaging analysis system for early detection of cervical cancer

Automatic Segmentation of Acute Leukemia Cells

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