A DBMS-Based Framework for Content-Based Retrieval and Analysis of Skin Ulcer Images in Medical Practice

Bedridden patients with skin lesions (ulcers) often do not have access to specialized clinic equipment. It is important to allow healthcare practitioners to use their smartphones to leverage information regarding the proper treatment to be carried. Existing applications require special equipment, such as heat sensors, or focus only on general information. To fulfill this gap, we propose ULEARn, a DBMS-based framework for the processing of ulcer images, providing tools to store and retrieve similar images of past cases. The proposed mobile application ULEARn-App allows healthcare practitioners to send a photo from a patient to ULEARn, and obtain a timely feedback that allows the improvement of procedures on therapeutic interventions. Experimental results of ULEARn and ULEARn-App using a real-world dataset showed that our tool can quickly respond to the required analysis and retrieval tasks, being up to 4.6 times faster than the specialist’ expected execution time.

[1]  F.G. Marchione,et al.  Approaches that use software to support the prevention of pressure ulcer: A systematic review , 2015, Int. J. Medical Informatics.

[2]  Luay Fraiwan,et al.  Mobile Application for Ulcer Detection , 2018, The open biomedical engineering journal.

[3]  Pascal Fua,et al.  SLIC Superpixels Compared to State-of-the-Art Superpixel Methods , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Rangaraj M. Rangayyan,et al.  Segmentation and analysis of the tissue composition of dermatological ulcers , 2010, CCECE 2010.

[5]  Agma J. M. Traina,et al.  SIREN: a similarity retrieval engine for complex data , 2006, VLDB.

[6]  Agma J. M. Traina,et al.  RAFIKI: Retrieval-Based Application for Imaging and Knowledge Investigation , 2018, 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS).

[7]  Agma J. M. Traina,et al.  ICARUS: Retrieving Skin Ulcer Images through Bag-of-Signatures , 2018, 2018 IEEE 31st International Symposium on Computer-Based Medical Systems (CBMS).

[8]  Gabriel Chodick,et al.  Synchronous video telemedicine in lower extremities ulcers treatment: A real-world data study , 2019, Int. J. Medical Informatics.

[9]  Joel J. P. C. Rodrigues,et al.  mULCER - A Mobile Ulcer Care Record Approach for Integrative Care , 2011, CENTERIS.

[10]  Fernando Pereira,et al.  MPEG-7 the generic multimedia content description standard, part 1 - Multimedia, IEEE , 2001 .

[11]  Neil D. Reeves,et al.  Fully convolutional networks for diabetic foot ulcer segmentation , 2017, 2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC).

[12]  Pavel Zezula,et al.  M-tree: An Efficient Access Method for Similarity Search in Metric Spaces , 1997, VLDB.

[13]  Ravi K. Chittoria Telemedicine for wound management , 2012, Indian journal of plastic surgery : official publication of the Association of Plastic Surgeons of India.

[14]  Ayman El-Baz,et al.  Classification of pressure ulcer tissues with 3D convolutional neural network , 2018, Medical & Biological Engineering & Computing.

[15]  Xiaoyong Du,et al.  MSQL: efficient similarity search in metric spaces using SQL , 2017, The VLDB Journal.

[16]  Agma J. M. Traina,et al.  FMI-SiR: A Flexible and Efficient Module for Similarity Searching on Oracle Database , 2010, J. Inf. Data Manag..

[17]  Christos Faloutsos,et al.  Slim-Trees: High Performance Metric Trees Minimizing Overlap Between Nodes , 2000, EDBT.

[18]  Mohammed J. Zaki Data Mining and Analysis: Fundamental Concepts and Algorithms , 2014 .