Segmentación y clasificación de células con leucemia a partir de información contextual en imágenes digitales
In this thesis, we propose a bone marrow cell contextual analysis methodology for the detection of acute leukemia subtypes. The first phase of the methodology focuses on the segmentation and identification of cellular elements from bone marrow images. In the second phase we perform feature extractio...
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| Format: | master thesis |
| Status: | Versión aceptada para publicación |
| Publication Date: | 2009 |
| Country: | México |
| Institution: | Instituto Nacional de Astrofísica, Óptica y Electrónica |
| Repository: | Repositorio Institucional del INAOE |
| Language: | English |
| OAI Identifier: | oai:inaoe.repositorioinstitucional.mx:1009/439 |
| Online Access: | http://inaoe.repositorioinstitucional.mx/jspui/handle/1009/439 |
| Access Level: | Open access |
| Keyword: | info:eu-repo/classification/Imágenes/Imaging info:eu-repo/classification/Segmentación de imagen/Image segmentation info:eu-repo/classification/Clasificación de imágenes/Image classification info:eu-repo/classification/Minería de datos/Data mining info:eu-repo/classification/cti/1 info:eu-repo/classification/cti/12 info:eu-repo/classification/cti/1203 |
| Summary: | In this thesis, we propose a bone marrow cell contextual analysis methodology for the detection of acute leukemia subtypes. The first phase of the methodology focuses on the segmentation and identification of cellular elements from bone marrow images. In the second phase we perform feature extraction to the cells images obtained in the first phase and use this information to classify the cells into leukemia subtypes. This classification can be used to diagnose patients. The segmentation algorithm uses as contextual information the color and texture of the image pixels to be able to separate the nucleus and cytoplasm of blood cells from bone marrow smear images, which show heterogeneous color and texture staining and a high cell population. The regions obtained from segmentation are later analyzed to identify the cells in the image. An additional algorithm to identify cells is proposed in this work. This algorithm also uses contextual information related to the color, shape, and containment proportion among regions to determine whether an analyzed ROI (Region of Interest) is labeled as a probable cell, nuclei, an overlapped nuclei or cell with other image elements or decide it is not a region of interest. If the cell identification algorithm determines that the ROI is overlapped with other elements, it divides the ROI by using a cell separation algorithm also proposed in this thesis. Once all of the ROIs are labeled, the cell is identified by associating its respective nuclei and cytoplasm, which is easily obtained by applying difference-set operations. The evaluation of the segmentation algorithm is carried out by comparing the identified regions with a manual segmentation. In general, an average accuracy of 95% was achieved in nucleus and cell segmentation using real bone marrow cells images. The accuracy is considered pretty good due to its high impact on the process of automatic classification of acute leukemia cells subtypes. In the cell classification phase we extract descriptive features (morphological, statistical, texture, size ratio and eigenvalues), to the nucleus and cytoplasm. These features were the input to several attribute selection and classification algorithms in order to generate patterns that facilitate the identification of the type and subtype of each acute leukemia cell in the image collection. |
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