Photographing Alopecia: How Many Pixels Are Needed for Clinical Evaluation?
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ORIGINAL PAPER
Photographing Alopecia: How Many Pixels Are Needed for Clinical Evaluation? Aylar Bayramova 1 & Tejas Mane 1 & Christian Hopkins 1 & Ying Zheng 1 & Temitayo A. Ogunleye 1 & Susan C. Taylor 1 & Leslie Castelo-Soccio 1,2,3 & Elena Bernardis 1 Received: 19 February 2020 / Revised: 17 August 2020 / Accepted: 14 September 2020 # Society for Imaging Informatics in Medicine 2020
Abstract Determining the minimum image resolution needed for clinical assessment is crucial for computational efficiency, image standardization, and storage needs alleviation. In this paper, we explore the image resolution requirements for the assessment of alopecia by analyzing how clinicians detect the presence of characteristics needed to quantify the disorder in the clinic. By setting the image resolution as a function of width of the patient’s head, we mimicked experiments conducted in the computer vision field to understand human perception in the context of scene recognition and object detection and asked 6 clinicians to identify the regions of interest on a set of retrospectively collected de-identified images at different resolutions. The experts were able to detect the presence of alopecia at very low resolutions, while significantly higher resolution was required to identify the presence of vellus-like hair. Furthermore, the accuracy with which alopecia was detected as a function of resolution followed the same trend as the one obtained when we classified normal versus abnormal hair density using a standard neural network architecture, hinting that the resolution needed by an expert human observer may also provide an upper bound for future image processing algorithms. Keywords Alopecia . Digital image interpretation . Image resolution requirements
Introduction Automated image analysis continues to gain traction for clinical dermatological applications, such as assessing psoriasis severity [1] and detection of melanoma [2]. In order to capture a clinical condition in a digital image to create efficient and accurate algorithms that would mimic the eye of a clinician, it is crucial to know what information is encoded at various image resolutions. While higher image resolution clearly provides more details, images of lower resolutions are not only
* Aylar Bayramova [email protected] 1
Department of Dermatology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
2
Department of Pediatrics, Section of Dermatology, The Children’s Hospital of Philadelphia, PA Philadelphia, USA
3
Department of Pediatrics, Perelman School of Medicine, University of Pennsylvania, PA Philadelphia, USA
sufficient to perform many common vision tasks but may also provide several computational advantages: faster algorithms, higher transfer speed for real-time analysis, and lower storage requirements. For natural scene photographs, for example, Torralba [3] showed that humans can reliably recognize a scene, as well as several objects contained within, from a thumbnail-size image of merely 32 × 32
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