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Multimedia systems are widely used in consumer electronics environments today, where humans can work and communicate through multi-sensory interfaces. Unfortunately smell detection and generation systems are not part of today's multimedia systems. In this' paper, we propose an Electronic Nose that can be used in a multimedia environment. The proposed electronic nose can detect a large number of smells and will have a significantly lower cost compared to the detection systems used in industrial applications (1). Index Terms--Electronic nose, multimedia systems, smell detection, smell sensors.

Accurate segmentation of overlapping nuclei is essential in determining nuclei count and evaluating the sub-cellular localization of protein biomarkers in image Cytometry and Histology. Current cellular segmentation algorithms generally lack fast and reliable methods for disambiguating clumped nuclei. In immuno-fluorescence segmentation, solutions to challenges including nuclei misclassification, irregular boundaries, and under-segmentation require reliable separation of clumped nuclei. This paper presents a fast and accurate algorithm for joint segmentation of cellular cytoplasm and nuclei incorporating procedures for reliably separating overlapping nuclei. The algorithm utilizes a combination of ideas and is a significant improvement on state-of-the-art algorithms for this application. First, an adaptive process that includes top-hat filtering, blob detection and distance transforms estimates the inverse illumination field and corrects for intensity non-uniformity. Minimum-error-thresholding based binarization augmented by statistical stability estimation is applied prior to seed-detection constrained by a distance-map-based scale-selection to identify candidate seeds for nuclei segmentation. The nuclei clustering step also incorporates error estimation based on statistical stability. This enables the algorithm to perform localized error correction. Final steps include artifact removal and reclassification of nuclei objects near the cytoplasm boundary as epithelial or stroma. Evaluation using 48 realistic phantom images with known ground-truth shows overall segmentation accuracy exceeding 96%. It significantly outperformed two state-of-the-art algorithms in clumped nuclei separation. Tests on 926 prostate biopsy images (326 patients) show that the segmentation improvement improves the predictive power of nuclei architecture features based on the minimum spanning tree algorithm. The algorithm has been deployed in a large scale pathology application.

Performance assessment of segmentation algorithms compares segmentation outputs to a handful of manually obtained ground-truth. This assumes that the ground-truth images are accurate, reliable and representative of the entire image set. In image cytometry, few ground-truth images are typically used because of the difficulty of manually segmenting images with large numbers of small objects. This violates the aforementioned assumptions. Automated methods of segmentation evaluation without ground-truth are needed. We describe a stable and reliable method for evaluating segmentation performance without ground-truth. Segmentation errors are either statistical or structural. Statistical errors reflect failure to account for random variations in pixel values while structural errors result from inadequate image description models. As statistical errors predominate image cytometry, our method focuses on statistical stability assessment. For any image-algorithm pair, we obtain multiple perturbed variants of the image by applying slight linear blur. We segment the image and its variants with the algorithm and determine the match between the output from the image and the output from its variants. We utilized 48 realistic phantom images with known ground-truth and four segmentation algorithms with large performance differences to assess the efficacy of the method. For each algorithm-image pair, we obtained a ground truth match score and four different statistical validation scores. Analyses show that statistical validation and ground-truth validation scores correlate in over 96% of cases. The statistical validation approach reduces segmentation review time and effort by over 99% and enables assessment of segmentation quality long after an algorithm has been deployed.

Automatic segmentation of cellular structures is an essential step in image cytology and histology. Despite substantial progress, better automation and improvements in accuracy and adaptability to novel applications are needed. In applications utilizing multi-channel immuno-fluorescence images, challenges include misclassification of epithelial and stromal nuclei, irregular nuclei and cytoplasm boundaries, and over and under-segmentation of clustered nuclei. Variations in image acquisition conditions and artifacts from nuclei and cytoplasm images often confound existing algorithms in practice. In this paper, we present a robust and accurate algorithm for jointly segmenting cell nuclei and cytoplasm using a combination of ideas to reduce the aforementioned problems. First, an adaptive process that includes top-hat filtering, Eigenvalues-of-Hessian blob detection and distance transforms is used to estimate the inverse illumination field and correct for intensity non-uniformity in the nuclei channel. Next, a minimum-error-thresholding based binarization process and seed-detection combining Laplacian-of-Gaussian filtering constrained by a distance-map-based scale selection is used to identify candidate seeds for nuclei segmentation. The initial segmentation using a local maximum clustering algorithm is refined using a minimum-error-thresholding technique. Final refinements include an artifact removal process specifically targeted at lumens and other problematic structures and a systemic decision process to reclassify nuclei objects near the cytoplasm boundary as epithelial or stromal. Segmentation results were evaluated using 48 realistic phantom images with known ground-truth. The overall segmentation accuracy exceeds 94%. The algorithm was further tested on 981 images of actual prostate cancer tissue. The artifact removal process worked in 90% of cases. The algorithm has now been deployed in a high-volume histology analysis application.

Tissue segmentation is one of the key preliminary steps in the morphometric analysis of tissue architecture. In multi-channel immunoflurorescent biomarker images, the primary segmentation steps consist of segmenting the nuclei (epithelial and stromal) and epithelial cytoplasm from 4',6-diamidino-2-phenylindole (DAPI) and cytokeratin 18 (CK18) biomarker images respectively. The epithelial cytoplasm segmentation can be very challenging due to variability in cytoplasm morphology and image staining. A robust and adaptive segmentation algorithm was developed for the purpose of both delineating the boundaries and separating thin gaps that separate the epithelial unit structures. This paper discusses novel methods that were developed for adaptive segmentation of epithelial cytoplasm and separation of epithelial units. The adaptive segmentation was performed by computing the non-epithelial background texture of every CK18 biomarker image. The epithelial unit separation was performed using two complementary techniques: a marker based, center-initialized watershed transform and a boundary initialized fast marching-watershed segmentation. The adaptive segmentation algorithm was tested on 926 CK18 biomarker biopsy images (326 patients) with limited background noise and 1030 prostatectomy images (374 patients) with noisy to very noisy background. The segmentation performance was measured using two different methods, namely; stability and background textural metrics. It was observed that the database of 1030 noisy prostatectomy images had a lower mean value (using stability and three background texture performance metrics) compared to the biopsy dataset of 926 images that had limited background noise. The average of all four performance metrics yielded 94.32% accuracy for prostatectomy images compared to 99.40% accuracy for biopsy images.