This paper presents the evaluation results of the methods submitted to Challenge US: Biometric Measurements from Fetal Ultrasound Images, a segmentation challenge held at the IEEE International Symposium on Biomedical Imaging 2012. The challenge was set to compare and evaluate current fetal ultrasound image segmentation methods. It consisted of automatically segmenting fetal anatomical structures to measure standard obstetric biometric parameters, from 2D fetal ultrasound images taken on fetuses at different gestational ages (21 weeks, 28 weeks, and 33 weeks) and with varying image quality to reflect data encountered in real clinical environments. Four independent sub-challenges were proposed, according to the objects of interest measured in clinical practice: abdomen, head, femur, and whole fetus. Five teams participated in the head sub-challenge and two teams in the femur sub-challenge, including one team who tackled both. Nobody attempted the abdomen and whole fetus sub-challenges. The challenge goals were two-fold and the participants were asked to submit the segmentation results as well as the measurements derived from the segmented objects. Extensive quantitative (region-based, distance-based, and Bland-Altman measurements) and qualitative evaluation was performed to compare the results from a representative selection of current methods submitted to the challenge. Several experts (three for the head sub-challenge and two for the femur sub-challenge), with different degrees of expertise, manually delineated the objects of interest to define the ground truth used within the evaluation framework. For the head sub-challenge, several groups produced results that could be potentially used in clinical settings, with comparable performance to manual delineations. The femur sub-challenge had inferior performance to the head sub-challenge due to the fact that it is a harder segmentation problem and that the techniques presented relied more on the femur's appearance.
The use of classifier-based object detection has found to be a promising approach in medical anatomy detection. In ultrasound images, the detection task is very challenging due to speckle, shadows and low contrast characteristic features. Typical detection algorithms that use purely intensitybased image features with an exhaustive scan of the image (sliding window approach) tend not to perform very well and incur a very high computational cost. The proposed approach in this paper achieves a significant improvement in detection rates while avoiding exhaustive scanning, thereby gaining a large increase in speed. Our approach uses the combination of local features from an intensity image and global features derived from a local phase-based image known as feature symmetry. The proposed approach has been applied to 2384 two-dimensional (2D) fetal ultrasound abdominal images for the detection of the stomach and the umbilical vein. The results presented show that it outperforms prior related work that uses only local or only global features.
Evaluation and benchmarking of skin detectors are challenging tasks because of multiple evaluation attributes and conflicting criteria. Although several evaluating and benchmarking techniques have been proposed, these approaches have many limitations. Fixing several attributes based on multi-attribute benchmarking approaches is particularly limited to reliable skin detection. Thus, this study aims to develop a new framework for evaluating and benchmarking skin detection on the basis of artificial intelligent models using multi-criteria analysis. For this purpose, two experiments are conducted. The first experiment consists of two stages: (1) discussing the development of a skin detector using multi-agent learning based on different color spaces to create a dataset of various color space samples for benchmarking and (2) discussing the evaluation and testing the developed skin detector according to multi-evaluation criteria (i.e. reliability, time complexity, and error rate within dataset) to create a decision matrix. The second experiment applies different decision-making techniques (AHP/SAW, AHP/MEW, AHP/HAW, AHP/TOPSIS, AHP/WSM, and AHP/WPM) to benchmark the results of the first experiment (i.e. the developed skin detector). Then, we discuss the use of the mean, standard deviation, and paired sample [Formula: see text]-test to measure the correlations among the different techniques based on ranking results.
The motivation of this paper is to analyse the efficiency and reliability of our proposed algorithm of femur length (FL) measurement for the estimation of gestational age. The automated methods are divided into the following components: threshold, segmentation and extraction. Each component is examined, and improvements are made with the objective of finding the optimal result for FL measurement. The methods are tested with a total of 200 different digitized ultrasound images from our database collection. Overall, the study shows that the watershed-based segmentation method combined with enhanced femur extraction algorithm and a 12 x 12 block averaging seed-point threshold method perform identically well with the expert measurements for every image tested and superior as compared to a previous method.
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