Gudigar, Anjan and Raghavendra, U and Devasia, Tom and Nayak, Krishnananda and Danisha, KE and Kamath, Gautam and Samanth, Jyothi and Pai, Umesh M and Nayak, Vidya and Tan, Ru Sa and Ciaccio, Edward J and Acharya, Rajendra U (2019) Global weighted LBP based entropy features for the assessment of pulmonary hypertension. Pattern Recognition Letters, 125. pp. 35-41. ISSN 0167-8655

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Abstract

Pulmonary hypertension (PH) is characterized by elevated pulmonary arterial pressure. Echocardiography, or cardiac ultrasound, is a helpful imaging tool to screen for PH. However, expert interpretation is re- quired for successful screening. Development of a more automated method for diagnosis of PH would be useful to minimize error, thereby improving patient health. This task is challenging and the literature per- taining to the problem is still nascent. In this paper, we propose a computer aided diagnosis (CAD) tool, using ultrasound images, to expedite the screening of PH. Textural components play a significant role in ultrasound imaging for the efficient identification of PH. The extraction of such features is accomplished by computing several entropy measurements over a globally weighted local binary pattern (LBP). There- after, the blend of ranked maximum and fuzzy entropy features are input to a support vector machine, resulting in a maximum accuracy of approximately 92%. A comparison with variants indicates improved performance of the proposed globally weighted LBP

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