Automatic Small Bowel Tumor Diagnosis by Using Multi-Scale Wavelet-Based Analysis in Wireless Capsule Endoscopy Images

BACKGROUND: Wireless capsule endoscopy has been introduced as an innovative, non-invasive diagnostic technique for evaluation of the gastrointestinal tract, reaching places where conventional endoscopy is unable to. However, the output of this technique is an 8 hours video, whose analysis by the expert physician is very time consuming. Thus, a computer assisted diagnosis tool to help the physicians to evaluate CE exams faster and more accurately is an important technical challenge and an excellent economical opportunity. METHOD: The set of features proposed in this paper to code textural information is based on statistical modeling of second order textural measures extracted from co-occurrence matrices. To cope with both joint and marginal non-Gaussianity of second order textural measures, higher order moments are used. These statistical moments are taken from the two-dimensional color-scale feature space, where two different scales are considered. Second and higher order moments of textural measures are computed from the co-occurrence matrices computed from images synthesized by the inverse wavelet transform of the wavelet transform containing only the selected scales for the three color channels. The dimensionality of the data is reduced by using Principal Component Analysis. RESULTS: The proposed textural features are then used as the input of a classifier based on artificial neural networks. Classification performances of 93.1% specificity and 93.9% sensitivity are achieved on real data. These promising results open the path towards a deeper study regarding the applicability of this algorithm in computer aided diagnosis systems to assist physicians in their clinical practice.

Leber Congenital Amaurosis: Comprehensive Survey of the Genetic Heterogeneity, Refinement of the Clinical Definition, and Genotype-Phenotype Correlations as a Strategy for Molecular Diagnosis

Leber congenital amaurosis (LCA) is the earliest and most severe form of all inherited retinal dystrophies, responsible for congenital blindness. Disease-associated mutations have been hitherto reported in seven genes. These genes are all expressed preferentially in the photoreceptor cells or the retinal pigment epithelium but they are involved in strikingly different physiologic pathways resulting in an unforeseeable physiopathologic variety. This wide genetic and physiologic heterogeneity that could largely increase in the coming years, hinders the molecular diagnosis in LCA patients. The genotyping is, however, required to establish genetically defined subgroups of patients ready for therapy. Here, we report a comprehensive mutational analysis of the all known genes in 179 unrelated LCA patients, including 52 familial and 127 sporadic (27/127 consanguineous) cases. Mutations were identified in 47.5% patients. GUCY2D appeared to account for most LCA cases of our series (21.2%), followed by CRB1 (10%), RPE65 (6.1%), RPGRIP1 (4.5%), AIPL1 (3.4%), TULP1 (1.7%), and CRX (0.6%). The clinical history of all patients with mutations was carefully revisited to search for phenotype variations. Sound genotype-phenotype correlations were found that allowed us to divide patients into two main groups. The first one includes patients whose symptoms fit the traditional definition of LCA, i.e., congenital or very early cone-rod dystrophy, while the second group gathers patients affected with severe yet progressive rod-cone dystrophy. Besides, objective ophthalmologic data allowed us to subdivide each group into two subtypes. Based on these findings, we have drawn decisional flowcharts directing the molecular analysis of LCA genes in a given case. These flowcharts will hopefully lighten the heavy task of genotyping new patients but only if one has access to the most precise clinical history since birth.