Venous thrombosis supervised image indexing and fuzzy retrieval

Anas Dahabiah (1, 2) , John Puentes (1, 2) , Basel Solaiman (1, 2)

1 ITI - Département Image et Traitement Information
2 LaTIM - Laboratoire de Traitement de l'Information Medicale

Clinical assessment of venous thrombosis (VT) is essential to evaluate the risk of size increase or embolism. Analyses like echogenecity and echostructure characterization, examine ancillary evidence to improve diagnosis. However, such analyses are inherently uncertain and operator dependent, adding enormous complexity to the task of indexing diagnosed images for medical practice support, by retrieving similar images, or to exploit electronic patient record repositories for data mining. This paper proposes a VT ultrasound image indexing and retrieval approach, which shows the suitability of neural network VT characterization, combined with a fuzzy similarity. Three types of image descriptors (sliding window, wavelet coefficients energy and co-occurrence matrix), are processed by three different neural networks, producing equivalent VT characterizations. Resulting values are projected on fuzzy membership functions and then compared with the fuzzy similarity. Compared to nominal and Euclidean distances, an experimental validation indicates that the fuzzy similarity increases image retrieval precision beyond the identification of images that belong to the same diagnostic class, taking into account the characterization result uncertainty, and allowing the user to privilege any particular feature.

Mots clés: Venous thrombosis (VT) volume; Echostructure characterization; Echogenecity

Domaines: Traitement du signal et de l'image [eess.SP]

Anas Dahabiah, John Puentes, Basel Solaiman. Venous thrombosis supervised image indexing and fuzzy retrieval. EMBS 2007 : 29th annual international conference of the IEEE engineering in medicine and biology society, August 22-26, Lyon, France, Aug 2007, Lyon, France. pp.4528 - 4531, ⟨10.1109/IEMBS.2007.4353346⟩. ⟨hal-02121490⟩