IEEE Transactions on Pattern Analysis and Machine Intelligence
Cervical Cancer Detection Using Colposcopic Images: a Temporal Approach
ENC '05 Proceedings of the Sixth Mexican International Conference on Computer Science
Knowledge-Guided Semantic Indexing of Breast Cancer Histopathology Images
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
BMEI '08 Proceedings of the 2008 International Conference on BioMedical Engineering and Informatics - Volume 02
Unsupervised Outlier Detection in Sensor Networks Using Aggregation Tree
ADMA '07 Proceedings of the 3rd international conference on Advanced Data Mining and Applications
ICCSA '08 Proceedings of the 2008 International Conference on Computational Sciences and Its Applications
Medical Image Classification Based on Fuzzy Support Vector Machines
ICICTA '08 Proceedings of the 2008 International Conference on Intelligent Computation Technology and Automation - Volume 02
On the Classification of Prostate Pathological Images Based on Gleason Score
IITAW '08 Proceedings of the 2008 International Symposium on Intelligent Information Technology Application Workshops
The Comparative Research on Image Segmentation Algorithms
ETCS '09 Proceedings of the 2009 First International Workshop on Education Technology and Computer Science - Volume 02
Classification Cervical Cancer Using Histology Images
ICCEA '10 Proceedings of the 2010 Second International Conference on Computer Engineering and Applications - Volume 01
Semi-automated Diagnosis of Melanoma through the Analysis of Dermatological Images
SIBGRAPI '10 Proceedings of the 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images
Linking image structures with medical ontology information
IWDM'06 Proceedings of the 8th international conference on Digital Mammography
Hi-index | 12.05 |
Molar pregnancy (also known as hydatidiform mole, hydatid mole, gestational trophoblastic disease) represents forms of abnormal conception caused by defective fertilisation resulting in excess expression of paternal genes in placental tissue. There are two forms of hydatidiform mole: complete (diploid androgenetic) and partial (paternal triploid), the distinction between which is important for determining appropriate prognosis and management of patients. Both complete and partial hydatidiform moles are associated with increased risk of development of malignant gestational trophoblastic tumours, the risk being much greater for complete hydatidiform moles. Whilst in most cases the diagnosis of these moles can be reliably achieved on morphological histological assessment, these represent a continuing diagnostic problem for histopathologists since in early pregnancy complete hydatidiform moles, partial hydatidiform moles and non-molar hydropic miscarriages may be difficult to distinguish. In this paper, we propose a computational image analysis approach guided by the knowledge of expert pathologists in identifying essential distinguishing morphological criteria. The approach, which combines Fuzzy C-Means clustering with hue, saturation and value colour space, shows promising results as it is able to classify successfully the villi into appropriate regions, namely trophoblast and stroma, and extract areas of blood. However, because of the marked variations in size, shape and outline of the villi, and trophoblast proliferation, both within and between cases, the analysis shows that there is no single criteria which can reliably classify these products of conception and a combination of criteria is required.