This article presents the design aspects and development processes to transform a general‐purpose mobile robotic platform into a semi‐autonomous agricultural robot sprayer focusing on user interfaces for teleoperation. The hardware and the software modules that must be installed onto the system are described, with particular emphasis on human–robot interaction. Details of the technology are given focusing on the user interface aspects. Two laboratory experiments and two studies in the field to evaluate the usability of the user interface provide evidence for the increased usability of a prototype robotic system. Specifically, the study aimed to empirically evaluate the type of target selection input device mouse and digital pen outperformed Wiimote in terms of usability. A field experiment evaluated the effect of three design factors: (a) type of screen output, (b) number of views, (c) type of robot control input device. Results showed that participants were significantly more effective but less efficient when they had multiple views, than when they had a single view. PC keyboard was also found to significantly outperform PS3 gamepad in terms of interaction efficiency and perceived usability. Heuristic evaluations of different user interfaces were also performed using research‐based HRI heuristics. Finally, a study on participants’ overall user experience found that the system was evaluated positively on the User Experience Questionnaire scales.

The paper presents the development of a computer-aided diagnostic (CAD) system for the early detection of endometrial cancer. The proposed CAD system supports reproducibility through texture feature standardization, standardized multifeature selection, and provides physicians with comparative distributions of the extracted texture features. The CAD system was validated using 516 regions of interest (ROIs) extracted from 52 subjects. The ROIs were equally distributed among normal and abnormal cases. To support reproducibility, the RGB images were first gamma corrected and then converted into HSV and YCrCb. From each channel of the gamma-corrected YCrCb, HSV, and RGB color systems, we extracted the following texture features: 1) statistical features (SFs), 2) spatial gray-level dependence matrices (SGLDM), and 3) gray-level difference statistics (GLDS). The texture features were then used as inputs with support vector machines (SVMs) and the probabilistic neural network (PNN) classifiers. After accounting for multiple comparisons, texture features extracted from abnormal ROIs were found to be significantly different than texture features extracted from normal ROIs. Compared to texture features extracted from normal ROIs, abnormal ROIs were characterized by lower image intensity, while variance, entropy, and contrast gave higher values. In terms of ROI classification, the best results were achieved by using SF and GLDS features with an SVM classifier. For this combination, the proposed CAD system achieved an 81% correct classification rate.

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The use of multiscale AM-FM analysis systems has been recently demonstrated in a variety of applications in medical image analysis. In all of these applications, a fixed filter-bank is used as a preprocessing step for estimating different AM-FM components from different scales. In this paper, for the first time, we introduce the use of an adaptive, multiscale AM-FM approach that searches for the optimal filter-bank specification for use in image classification. We demonstrate an example application in hysteroscopy imaging, for identification of gynaecological cancer, where the optimal filter-bank turns out to be circularly symmetric.

In this study we present an integrated system for supporting the diagnosis of endometrial cancer. The system consists of an electronic patient record that incoporates a hysteroscopy imaging CAD system for the early detection of endometrial cancer. The electronic patient record is based on information collected from: appointments, patient info, hysteroscopy reporting and pharmacy. The CAD system is based on ROI manual or semi-automated extraction, texture feature computation and SVM and C4.5 classification into normal/abnormal. The highest percentage of correct classifications score (%CC) for the SVM classifier was 79°.4 for the YCrCb color system using the SF+SGLDS texture feature sets for differentiating between normal vs abnormal ROIs. The C4.5 algorithm gave slightly lower classification scores, but also classification rules. The proposed system offers an integrated platform to the physician for assessing suspicious areas of endometrial cancer. However, further work is needed to validate the system with more cases and more users of the prototype.