Hyperspectral colon tissue cell classification

A novel algorithm to discriminate between normal and malignant tissue cells of the human colon is presented. The microscopic level images of human colon tissue cells were acquired using hyperspectral imaging technology at contiguous wavelength intervals of visible light. While hyperspectral imagery data provides a wealth of information, its large size normally means high computational processing complexity. Several methods exist to avoid the so-called curse of dimensionality and hence reduce the computational complexity. In this study, we experimented with Principal Component Analysis (PCA) and two modifications of Independent Component Analysis (ICA). In the first stage of the algorithm, the extracted components are used to separate four constituent parts of the colon tissue: nuclei, cytoplasm, lamina propria, and lumen. The segmentation is performed in an unsupervised fashion using the nearest centroid clustering algorithm. The segmented image is further used, in the second stage of the classification algorithm, to exploit the spatial relationship between the labeled constituent parts. Experimental results using supervised Support Vector Machines (SVM) classification based on multiscale morphological features reveal the discrimination between normal and malignant tissue cells with a reasonable degree of accuracy

Blind Curvelet based Denoising of Seismic Surveys in Coherent and Incoherent Noise Environments

The localized nature of curvelet functions, together with their frequency and dip characteristics, makes the curvelet transform an excellent choice for processing seismic data. In this work, a denoising method is proposed based on a combination of the curvelet transform and a whitening filter along with procedure for noise variance estimation. The whitening filter is added to get the best performance of the curvelet transform under coherent and incoherent correlated noise cases, and furthermore, it simplifies the noise estimation method and makes it easy to use the standard threshold methodology without digging into the curvelet domain. The proposed method is tested on pseudo-synthetic data by adding noise to real noise-less data set of the Netherlands offshore F3 block and on the field data set from east Texas, USA, containing ground roll noise. Our experimental results show that the proposed algorithm can achieve the best results under all types of noises (incoherent or uncorrelated or random, and coherent noise)

On Importance of Acoustic Backscatter Corrections for Texture-based Seafloor Characterization

Seafloor segmentation and characterization based on local textural properties of acoustic backscatter has been a subject of research since 1980s due to the highly textured appearance of sonar images. The approach consists of subdivision of sonar image in a set of patches of certain size and calculation of a vector of features reflecting the patch texture. Advance of multibeam echosounders (MBES) allowed application of texture-based techniques to real geographical space, and predicted boundaries between acoustic facies became experimentally verifiable. However, acoustic return from uncalibrated MBES produces artifacts in backscatter mosaics, which in turn affects accuracy of delineation. Development of Geocoder allowed creation of more visually consistent images, and reduced the number of factors influencing mosaic creation. It is intuitively clear that more accurate backscatter mosaics lead to more reliable classification results. However, this statement has never been thoroughly verified. It has not been investigated which corrections are important for texture-based characterization and which are not essential. In this paper the authors are investigating the Stanton Banks common dataset. Raw data files from the dataset have been processed by the Geocoder at different levels of corrections. Each processing resulted in a backscatter mosaic demonstrating artifacts of different levels of severity. Mosaics then underwent textural analysis and unsupervised classification using Matlab package SonarClass. Results of seafloor characterization corresponding to varying levels of corrections were finally compared to the one generated by the best possible mosaic (the one embodying all the available corrections), providing an indicator of classification accuracy and giving guidance about which mosaic corrections are crucial for acoustic classification and which could be safely ignored

BLADE: Filter Learning for General Purpose Computational Photography

The Rapid and Accurate Image Super Resolution (RAISR) method of Romano, Isidoro, and Milanfar is a computationally efficient image upscaling method using a trained set of filters. We describe a generalization of RAISR, which we name Best Linear Adaptive Enhancement (BLADE). This approach is a trainable edge-adaptive filtering framework that is general, simple, computationally efficient, and useful for a wide range of problems in computational photography. We show applications to operations which may appear in a camera pipeline including denoising, demosaicing, and stylization