A probabilistic framework for simultaneous segmentation and classification of multiple cells in multi-marker microscopy images

Shenoy, Renuka; Shih, Min-Chi; Rose, Kenneth

doi:10.1109/isbi.2015.7164094

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“…Then, a probability map indicating change amount is constructed using statistical properties of segmented time sequences. In study [6], a Hidden Markov Model (HMM) based segmentation framework is developed to be used with sophisticated microscopy data, in which many different categories of biological material exist. Super pixel level segments obtained from HMM operation are combined into object level segments by the help of transition probabilities.…”

Section: Introductionmentioning

confidence: 99%

Classification of human carcinoma cells using multispectral imagery

et al. 2016

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In this paper, we present a technique for automatically classifying human carcinoma cell images using textural features. An image dataset containing microscopy biopsy images from different patients for 14 distinct cancer cell line type is studied. The images are captured using a RGB camera attached to an inverted microscopy device. Texture based Gabor features are extracted from multispectral input images. SVM classifier is used to generate a descriptive model for the purpose of cell line classification. The experimental results depict satisfactory performance, and the proposed method is versatile for various microscopy magnification options.

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Section: Introductionmentioning

confidence: 99%