The interaction of light with molecular conduction junction is attracting growing interest as a challenging experimental and theoretical problem on one hand, and because of its potential application as a characterization and control tool on the other. From both its scientific aspect and technological potential it stands at the interface of two important fields: molecular electronics and molecular plasmonics. I shall review the present state of the art of this field and our work on optical response, Raman scattering, temperature measurements, light generation and photovoltaics in such systems.
We present a pseudoparticle nonequilibrium Green function formalism as a tool to study the coupling between plasmons and excitons in nonequilibrium molecular junctions. The formalism treats plasmon-exciton couplings and intra-molecular interactions exactly, and is shown to be especially convenient for exploration of plasmonic absorption spectrum of plexitonic systems, where combined electron and energy transfers play an important role. We demonstrate the sensitivity of the molecule-plasmon Fano resonance to junction bias and intra-molecular interactions (Coulomb repulsion and intra-molecular exciton coupling). The electromagnetic theory is used in order to derive self-consistent ¯eld-induced coupling terms between the molecular and the plasmon excitations. Our study opens a way to deal with strongly interacting plasmon-exciton systems in nonequilibrium molecular devices.
PURPOSE: To propose a method for Parametric Statistical Weights (PSW) estimations and analyze its statistical impact in Computer-Aided Diagnosis Imaging Systems based on a Relative Similarity (CADIRS) classification approach.
MATERIALS AND METHODS: A Multifactor statistical method was developed and applied for Parametric Statistical Weights calculations in CADIRS. The implemented PSW method was used for statistical estimations of PSW impact when applied to a clinically validated breast ultrasound digital database of 332 patients' cases with biopsy proven findings. The method is based on the assumption that each parameter used in Relative Similarity (RS) classifier contributes to the deviation of the diagnostic prediction proportionally to the normalized value of its coefficient of multiple regression. The calculated by CADIRS Relative Similarity values with and without PSW were statistically estimated, compared and analyzed (on subset of cases) using classic Receiver Operator Characteristic (ROC) analysis methods.
RESULTS: When CADIRS classification scheme was augmented with PSW the Relative Similarity the calculated values were 2-5% higher in average. Numeric estimations of PSW allowed decomposition of statistical significance for each component (factor) and its impact on similarity to the diagnostic results (biopsy proven).
CONCLUSION: Parametric Statistical Weights in Computer-Aided Diagnosis Imaging Systems based on a Relative Similarity classification approach can be successfully applied in an effort to enhance overall classification (including scoring) outcomes. For the analyzed cohort of 332 cases the application of PSW increased Relative Similarity to the retrieved templates with known findings by 2-5% in average.
A well-defined rule-based system has been developed for scoring 0-5 the Level of Suspicion (LOS) based on qualitative lexicon describing the ultrasound appearance of breast lesion. The purposes of the research are to asses and select one of the automated LOS scoring quantitative methods developed during preliminary studies in benign biopsies reduction. The study has used Computer Aided Imaging System (CAIS) to improve the uniformity and accuracy of applying the LOS scheme by automatically detecting, analyzing and comparing breast masses. The overall goal is to reduce biopsies on the masses with lower levels of suspicion, rather that increasing the accuracy of diagnosis of cancers (will require biopsy anyway). On complex cysts and fibroadenoma cases experienced radiologists were up to 50% less certain in true negatives than CAIS. Full correlation analysis was applied to determine which of the proposed LOS quantification methods serves CAIS accuracy the best. This paper presents current results of applying statistical analysis for automated LOS scoring quantification for breast masses with known biopsy results. It was found that First Order Ranking method yielded most the accurate results. The CAIS system (Image Companion, Data Companion software) is developed by Almen Laboratories and was used to achieve the results.
Breast biopsy serves as the key diagnostic tool in the evaluation of breast masses for malignancy, yet the procedure affects patients physically and emotionally and may obscure results of future mammograms. Studies show that high quality ultrasound can distinguish a benign from malignant lesions with accuracy, however, it has proven difficult to teach and clinical results are highly variable. The purpose of this study is to develop a means to optimize an automated Computer Aided Imaging System (CAIS) to assess Level of Suspicion (LOS) of a breast mass. We examine the contribution of 15 object features to lesion classification by calculating the Wilcoxon area under the ROC curve, AW, for all combinations in a set of 146 masses with known findings. For each interval A, the frequency of appearance of each feature and its combinations with others was computed as a means to find an “optimum” feature vector. The original set of 15 was reduced to 6 (area, perimeter, diameter ferret Y, relief, homogeneity, average energy) with an improvement from Aw=0.82∓0.04 for the original 15 to Aw=0.93∓0.02 for the subset of 6, p=0.03. For comparison, two sub-specialty mammography radiologists also scored the images for LOS resulting in Az of 0.90 and 0.87. The CAIS performed significantly higher, p=0.02.
Research studies indicate that careful application of breast ultrasound is capable of reducing the number of unnecessary biopsies by 40% with potential cost savings of as much as $1 billion per year in the U.S. A well-defined rule-based system has been developed for scoring the Level of Suspicion (LOS) based on parameters describing the ultrasound appearance of breast lesion. Acceptance and utilization of LOS is increasing but it has proven difficult to teach the method and many radiologists have felt uncomfortable with the number of benign and malignant masses that overlap in appearance. In practice, the quality of breast ultrasound is highly operator dependent, it is often difficult to reproduce a finding and there is high variability of lesion description and assessment between radiologists. The goal of this research is to improve the uniformity and accuracy of applying the LOS scheme by automatically detecting, analyzing and comparing breast masses using sophisticated software developed for satellite imagery applications. The aim is to reduce biopsies on the masses with lower levels of suspicion, rather that increasing the accuracy of diagnosis of cancers, which will require biopsy anyway. In this paper we present our approach to develop a system to process, segment, analyze and classify medical images based on information content. A feasibility study was completed in a digital database of biopsy-proven image files from 46 women retrieved chronologically from our image library. Segmentation and classification were sufficiently accurate to correctly group all benign cystic masses, all benign solid masses and all solid malignant masses. The image analysis, computer-aided detection and image classification software system Image Companion developed by Almen Laboratories, Inc. was used to achieve the presented results.