Background: Patients with allergic rhinoconjunctivitis are susceptible to both nasal and ocular symptoms. The conjunctival provocation test (CPT) is an established diagnostic procedure used in allergic rhinoconjunctivitis, particularly to document a patient's current reactivity to allergens. To date, there are no international guidelines defining the CPT. No approved evaluation method exists for interpreting CPT results. This paper aims to establish the digital analysis of macroimages as an objective, validated and standardized method for interpreting CPT results. Methods: In a clinical immunotherapy trial with 155 patients, treatment progress was documented based on the CPT. Local investigators used a symptom score to grade tearing, reddening and the patients' subjective perception of symptoms (mucosal irritation). A central observer rated conjunctival hyperemia via digital photography. Digital image analysis software was utilized to determine conjunctival hyperemia. Results: Spearman's correlation between the local investigators' and the central observer's ratings was r = 0.729 (p < 0.001); the percentage of total agreement was 48% (based on 739 photos). Digital image analysis (based on 48 photos) had a high percentage of total agreement with the central observer's ratings (69%) but a low percentage of total agreement with the investigators' ratings (38%). The corresponding correlations were r = 0.264 and 0.064, respectively. Conclusion: Photography-based rating by a central observer may represent a valuable supplement to the local investigator's assessment for making an objective evaluation of CPT results. Digital image analysis possesses the potential of being an objective evaluation method compared to the wide-spread subjective evaluation by the investigators.

Computer-aided diagnosis is developed for assessment of allergic rhinitis/rhinoconjunctivitis measuring the relative redness of sclera under application of allergen solution. Images of the patient's eye are taken using a commercial digital camera. The iris is robustly localized using a gradient-based Hough circle transform. From the center of the pupil, the region of interest within the sclera is extracted using geometric anatomy-based apriori information. The red color pixels are extracted thresholding in the hue, saturation and value color space. Then, redness is measured by taking mean of saturation projected into zero hue. Evaluation is performed with 98 images taken from 14 subjects, 8 responders and 6 non-responders, which were classified according to an experienced otorhinolaryngologist. Provocation is performed with 100, 1,000 and 10,000 AU/ml allergic solution and normalized to control images without provocation. The evaluation yields relative redness of 1.01, 1.05, 1.30 and 0.95, 1.00, 0.96 for responders and non-responders, respectively. Variations in redness measurements were analyzed according to alteration of parameters of the image processing chain proving stability and robustness of our approach. The results indicate that the method improves visual inspection and may be suitable as reliable surrogate endpoint in controlled clinical trials.

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Computer-aided detection is an integral part of medical image evaluation process because examination of each image takes a long time and generally experts’ do not have enough time for the elimination of images with motion artifact (blurred images). Computer-aided detection is required for both increasing accuracy rate and saving experts’ time. Large intestine does not have straight structure thus camera of the colonoscopy should be moved continuously to examine inside of the large intestine and this movement causes motion artifact on colonoscopy images. In this study, images were selected from open-source colonoscopy videos and obtained at Kayseri Training and Research Hospital. Totally 100 images were analyzed half of which were clear. Firstly, a modified version of histogram equalization was applied in the pre-processing step to all images in our dataset, and then, used Laplacian, wavelet transform (WT), and discrete cosine transform-based (DCT) approaches to extract features for the discrimination of images with no artifact (clear) and images with motion artifact. The Laplacian-based feature extraction method was used for the first time in the literature on colonoscopy images. The comparison between Laplacian-based features and previously used methods such as WT and DCT has been performed. In the classification phase of our study, support vector machines (SVM), linear discriminant analysis (LDA), and k nearest neighbors (k-NN) were used as the classifiers. The results showed that Laplacian-based features were more successful in the detection of images with motion artifact when compared to popular methods used in the literature. As a result, a combination of features extracted using already existing approaches (WT and DCT) and the Laplacian-based methods reached 85% accuracy levels with SVM classification approach