Multimodal medical case retrieval using the Dezert-Smarandache theory.

International audienceMost medical images are now digitized and stored with semantic information, leading to medical case databases. They may be used for aid to diagnosis, by retrieving similar cases to those in examination. But the information are often incomplete, uncertain and sometimes conflicting, so difficult to use. In this paper, we present a Case Based Reasoning (CBR) system for medical case retrieval, derived from the Dezert-Smarandache theory, which is well suited to handle those problems. We introduce a case retrieval specific frame of discernment theta, which associates each element of theta with a case in the database; we take advantage of the flexibility offered by the DSmT's hybrid models to finely model the database. The system is designed so that heterogeneous sources of information can be integrated in the system: in particular images, indexed by their digital content, and symbolic information. The method is evaluated on two classified databases: one for diabetic retinopathy follow-up (DRD) and one for screening mammography (DDSM). On these databases, results are promising: the retrieval precision at five reaches 81.8% on DRD and 84.8% on DDSM

Visual Outcomes After Cataract Surgery:Multifocal Versus Monofocal Intraocular Lenses

PURPOSE: To evaluate visual outcomes, spectacle independence, and quality of life among nonastigmatic and astigmatic patients who received AcrySof IQ ReSTOR toric or nontoric multifocal intraocular lenses (IOLs) (Alcon Laboratories, Fort Worth, TX) compared with those who received commercially available nontoric monofocal IOLs after bilateral cataract removal. METHODS: This randomized, patient- and observer-technician-masked study was conducted at 20 sites in Europe. Patients were randomized to receive monofocal (nontoric only) or multifocal (nontoric or toric, as needed) IOLs. Primary efficacy endpoints included percentage of patients achieving binocular uncorrected distance and near acuity of 0.1 logMAR or better (20/25 Snellen), spectacle independence, and scores on the National Eye Institute Refractive Error and Quality of Life questionnaire domains. Safety endpoints included adverse events and refractive error within 0.5 and 1.0 diopters. RESULTS: In the multifocal group (n = 108) versus the monofocal group (n = 100), significantly more patients achieved uncorrected distance and near acuity of 0.1 logMAR or better (45.7% vs 2.1%; P < .0001) and spectacle independence (73.3% vs 25.3%; P < .0001) at 6 months. The percentage of patients who achieved uncorrected distance visual acuity of 20/40 or better at 6 months was 92% in the multifocal group and 97% in the monofocal group. National Eye Institute Refractive Error and Quality of Life scores were significantly better for dependence on correction in the multifocal group (P < .0001) and for glare in the monofocal group (P = .0157); other domain scores were similar between groups. No significant trends in study device-related adverse events were observed. CONCLUSIONS: Monofocal and multifocal IOLs provided good clinical outcomes. More patients receiving multifocal IOLs attained better uncorrected visual acuity at a range of distances and spectacle independence compared with patients who received monofocal IOLs. Monofocal IOLs were associated with better patient-reported scores for glare compared with multifocal IOLs; however, scores for patient satisfaction were significantly better in the multifocal group

Content based Medical Image Retrieval: use of Generalized Gaussian Density to model BEMD's IMF.

In this paper, we address the problem of medical ddiagnosis aid through content based image retrieval methods. We propose to characterize images without extracting local features, by using global information extracted from the image Bidimensional Empirical Mode Decomposition (BEMD). This method decompose image into a set of functions named Intrinsic Mode Functions (IMF) and a residu. The generalized Gaussian density function (GGD) is used to represent the coefficients derived from each IMF, and the Kullback–Leibler Distance (KLD) compute the similarity between GGDs. Retrieval efficiency is given for different databases including a diabetic retinopathy, and a face database. Results are promising: the retrieval efficiency is higher than 85% for some cases

Supervised stereo visual acuity tests implemented on 3D TV monitors

International audienceIn this paper we discuss under which conditions standard stereo visual acuity tests can be implemented on 3D TV monitors. In particular, we emphasize the role of environmental lighting conditions, on the measurement of the stereo visual acuity, when using conventional 3D tests, such as Wirt stereotests. We investigate the impact of parameters such as luminance, backlight and contrast when these tests are implemented on 3D TV monitors. We demonstrate that some deviations are observed when modifying the room luminance and the type of displays used (e.g. plasma (PDP) or liquid crystal (LCD) displays). Our measurements carried out on an human sample are supervised by pupil size measurements, using an eyes-tracker, enabling a better interpretation of the results. Finally, we discuss the benefit of using 3D tools to implement stereo visual acuity measurements

Multimedia data mining for automatic diabetic retinopathy screening

International audience— This paper presents TeleOphta, an automatic sys-tem for screening diabetic retinopathy in teleophthalmology networks. Its goal is to reduce the burden on ophthalmologists by automatically detecting non referable examination records, i.e. examination records presenting no image quality problems and no pathological signs related to diabetic retinopathy or any other retinal pathology. TeleOphta is an attempt to put into practice years of algorithmic developments from our groups. It combines image quality metrics, specific lesion detectors and a generic pathological pattern miner to process the visual content of eye fundus photographs. This visual information is further combined with contextual data in order to compute an abnormality risk for each examination record. The TeleOphta system was trained and tested on a large dataset of 25,702 examination records from the OPHDIAT screening network in Paris. It was able to automatically detect 68% of the non referable examination records while achieving the same sensitivity as a second ophthalmologist. This suggests that it could safely reduce the burden on ophthalmologists by 56%