Examining palpebral conjunctiva for anemia assessment with image processing methods

Chen, Yiming; Miaou, Shaou-Gang; Bian, Hongyu

doi:10.1016/j.cmpb.2016.08.025

Cited by 36 publications

(27 citation statements)

References 10 publications

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“…Researchers from related works used hundreds of samples for conducting and validating their study [45][46][47][48][49][50] and most of the conjunctiva images were not publicly available such as retinal fundus images. The acquisition system consists of a macro-lens assembled into a specially designed, 3D-printed lightened spacer and a smartphone.…”

Section: U-net Based Conjunctiva Segmentation Model (Unbcsm)mentioning

confidence: 99%

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Kasiviswanathan¹,

Bai²,

Simone

et al. 2020

Electronics

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Technology is changing the future of healthcare, technology-supported non-invasive medical procedures are more preferable in the medical diagnosis. Anemia is one of the widespread diseases affecting the wellbeing of individuals around the world especially childbearing age women and children and addressing this issue with the advanced technology will reduce the prevalence in large numbers. The objective of this work is to perform segmentation of the conjunctiva region for non-invasive anemia detection applications using deep learning. The proposed U-Net Based Conjunctiva Segmentation Model (UNBCSM) uses fine-tuned U-Net architecture for effective semantic segmentation of conjunctiva from the digital eye images captured by consumer-grade cameras in an uncontrolled environment. The ground truth for this supervised learning was given as Pascal masks obtained by manual selection of conjunctiva pixels. Image augmentation and pre-processing was performed to increase the data size and the performance of the model. UNBCSM showed good segmentation results and exhibited a comparable value of Intersection over Union (IoU) score between the ground truth and the segmented mask of 96% and 85.7% for training and validation, respectively.

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Section: U-net Based Conjunctiva Segmentation Model (Unbcsm)mentioning

confidence: 99%

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Kasiviswanathan¹,

Bai²,

Simone

et al. 2020

Electronics

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“…In our experiment, we use the same palpebral conjunctiva images in [ 13 , 14 ] as our database. There are a total of 100 images in which 40 of them are labeled as anemia samples and 60 of them are labeled as nonanemia samples according to the threshold set at 11 g/dL [ 13 , 14 ]. In other words, those with an Hb level higher than 11 g/dL are labeled as anemia patients in this paper; otherwise, they would be nonanemic patients.…”

Section: Resultsmentioning

confidence: 99%

“…In our previous works ([ 13 , 14 ]), we enlarged the images to 500 × 500 pixels in order to observe the image texture better and facilitate the further processing of images (e.g., extract entropy features). The two articles ([ 13 , 14 ]) use the same database, where the original images are usually not square and their sizes vary significantly, but all of them are less than 150 × 150 pixels. All available images are resized (enlarged) to a fixed size of 500 × 500 pixels using relevant MATLAB functions.…”

Section: Resultsmentioning

confidence: 99%

“…Few preprocessing efforts have been made specifically for noninvasive anemia examining. In [ 13 , 14 ], a preprocessing algorithm based on k-mean clustering is tailored to remove the bright reflection spot in the image. However, this is just one type of special noises due to camera setting and/or ambient light condition and the k-mean clustering approach is not general enough to deal with all possible noise types.…”

Section: Introductionmentioning

confidence: 99%

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A Kalman Filtering and Nonlinear Penalty Regression Approach for Noninvasive Anemia Detection with Palpebral Conjunctiva Images

Chen

Miaou

2017

Journal of Healthcare Engineering

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Noninvasive medical procedures are usually preferable to their invasive counterparts in the medical community. Anemia examining through the palpebral conjunctiva is a convenient noninvasive procedure. The procedure can be automated to reduce the medical cost. We propose an anemia examining approach by using a Kalman filter (KF) and a regression method. The traditional KF is often used in time-dependent applications. Here, we modified the traditional KF for the time-independent data in medical applications. We simply compute the mean value of the red component of the palpebral conjunctiva image as our recognition feature and use a penalty regression algorithm to find a nonlinear curve that best fits the data of feature values and the corresponding levels of hemoglobin (Hb) concentration. To evaluate the proposed approach and several relevant approaches, we propose a risk evaluation scheme, where the entire Hb spectrum is divided into high-risk, low-risk, and doubtful intervals for anemia. The doubtful interval contains the Hb threshold, say 11 g/dL, separating anemia and nonanemia. A suspect sample is the sample falling in the doubtful interval. For the anemia screening purpose, we would like to have as less suspect samples as possible. The experimental results show that the modified KF reduces the number of suspect samples significantly for all the approaches considered here.

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“…Algorithms select the region of interest (ROI), filter and process the images, remove the noise, and measure the intensity of the pixels providing a quantitative result. Chen et al 18 tested different algorithms on 100 images of the palpebral conjunctiva, and the algorithms showed sensitivity ranging from 62 to 78% and specificity from 83 to 90%.…”

Section: Noninvasive Diagnosis Of Anemia Using the Smartphonementioning

confidence: 99%

Mobile Health (mHealth) and Advances in Noninvasive Diagnosis of Anemia: An Overview

Mazzu-Nascimento

Evangelista

Abubakar

et al. 2020

International Journal of Nutrology

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Anemia is a public health problem that can have different causes, such as iron deficiency, vitamin deficiency, inflammation, hemolytic anemias, and anemias associated with bone marrow disease. Anemia shows a decrease in the concentration of hemoglobin, a pigmented molecule in the erythrocytes. The objectives of this review were to highlight the impact of nutritional factors on morbidity and mortality caused by anemia and to present different non-invasive approaches that use a smartphone to analyze hemoglobin levels to detect anemia. According to the records of the Brazilian Unified Health System (SUS, in the Portuguese acronym), ∼ 440,000 people checked in hospitals due to anemia between January 2015 and April 2020, with 215,000 deaths. The government spent ∼ 294 million Brazilian Reais (more than 50 million US dollars) on anemia hospitalization cases during this period. There is a worldwide search to provide noninvasive diagnostics and mobile health (mHealth) tools to help diagnosing anemia. The smartphone appears to be a viable device to detect anemia by a camera with colorimetric analysis of images providing a quantitative, instantaneous, and noninvasive result. These images can be obtained as a photograph or extracted from video frames. The review presents three different methods of detecting anemia using a smartphone: i) photoplethysmograph from video obtained from the tip of the index finger, ii) photo of the palpebral conjunctiva, and iii) fingernail photo app. Therefore, it seems urgent that these approaches may be applied in routine clinical diagnosis to allow remote, needy, low-tech locations to have access to anemia screening.

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Examining palpebral conjunctiva for anemia assessment with image processing methods

Cited by 36 publications

References 10 publications

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

Semantic Segmentation of Conjunctiva Region for Non-Invasive Anemia Detection Applications

A Kalman Filtering and Nonlinear Penalty Regression Approach for Noninvasive Anemia Detection with Palpebral Conjunctiva Images

Mobile Health (mHealth) and Advances in Noninvasive Diagnosis of Anemia: An Overview

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