Kazuma Shinoda

Lossless and lossy coding for multispectral image based on sRGB standard and residual components

In this paper, we present a multispectral image (MSI) compression method using a lossless and lossy coding scheme, which focuses on the seamless coding of the RGB bit stream to enhance the usability of the MSI. The proposed method divides the MSI data into two components: RGB and residual. The RGB component is extracted from the MSI by using the XYZ color matching functions, a color conversion matrix, and a gamma curve. The original MSI is estimated by an RGB data encoder and the difference between the original and the estimated MSI, which is referred to as the residual component in this paper. Next, the RGB and residual components are encoded by using JPEG2000, and progressive decoding is achieved from the losslessly encoded code stream. Experimental results show that a high-quality RGB image can be obtained at a low bit rate with primary encoding of the RGB component. In addition, by using the proposed method, the quality of a spectrum can be improved by decoding the residual data, and the quality is comparable to that obtained by using JPEG2000. The lossless compression ratio obtained by using this method is also similar to that obtained by using JPEG2000 with the integer Karhunen-Loeve transform.

Quality metric for filter arrangement in a multispectral filter array

Mosaicked color filter arrays and demosaicking methods for multispectral images have been proposed in recent years. Several studies have evaluated the multispectral filter array (MSFA) pattern, but a method to optimize the pattern has not been proposed. We focus on the spatial arrangement of MSFAs to improve the demosaicked image quality. To evaluate the filter arrangement, we propose a new metric that uses both the spatial and spectral correlation of the filters. The arrangement is optimized by using simulated annealing and the proposed metric. An advantage of the proposed metric and its optimization is that they do not need to use image data. Experimental results show that the new metric is proportional to the peak-to-signal noise ratio (PSNR) and that a higher PSNR can be obtained by minimizing the metric using simulated annealing.

Multispectral demosaicking algorithm based on inter-channel correlation

Conventional RGB image acquisition employs an image capturing system that utilizes color filter array (CFA) technology; however, it is limited in its ability to represent visible colors. Multispectral imaging based on filter array architecture is required for general image capturing because of its exquisite color representation system. However, it has several issues associated with spatial and spectral resolution. In this paper, we propose a new demosaicking method that improves reconstructed image quality by considering inter-channel correlation. Our proposed method strengthens the cross-correlation of demosaicked channels by repeating interpolations. Experimental results show that our proposed method generates better quality reconstructed images than conventional methods.

Lossless-by-Lossy Coding for Scalable Lossless Image Compression

This paper presents a method of scalable lossless image compression by means of lossy coding. A progressive decoding capability and a full decoding for the lossless rendition are equipped with the losslessly encoded bit stream. Embedded coding is applied to large-amplitude coefficients in a wavelet transform domain. The other wavelet coefficients are encoded by a context-based entropy coding. The proposed method slightly outperforms JPEG-LS in lossless compression. Its rate-distortion performance with respect to progressive decoding is close to that of JPEG2000. The spatial scalability with respect to resolution is also available.

A Lossless-by-Lossy Approach to Lossless Image Compression

This paper proposes a method of lossless image coding by the aid of lossy image coding. It aims at an improvement in the compression efficiency. We apply a kind of embedded coding to large coefficients in magnitude in a wavelet transform domain. The other wavelet coefficients are encoded by a context-based entropy coding. The result slightly outperforms the compression efficiency in JPEG-LS.

An accurate method of extracting fat droplets in liver images for quantitative evaluation

The steatosis in liver pathological tissue images is a promising indicator of nonalcoholic fatty liver disease (NAFLD) and the possible risk of hepatocellular carcinoma (HCC). The resulting values are also important for ensuring the automatic and accurate classification of HCC images, because the existence of many fat droplets is likely to create errors in quantifying the morphological features used in the process. In this study we propose a method that can automatically detect, and exclude regions with many fat droplets by using the feature values of colors, shapes and the arrangement of cell nuclei. We implement the method and confirm that it can accurately detect fat droplets and quantify the fat droplet ratio of actual images. This investigation also clarifies the effective characteristics that contribute to accurate detection.

Reversible data hiding based on two-dimensional histogram and generalized histogram shifting

In this paper, we propose a reversible data hiding (RDH) method based on a two-dimensional (2D) histogram and generalized histogram shifting (GHS). First, a cover image is decomposed into wavelet subbands using the integer-to-integer wavelet transform (I2I-WT). Then, the 2D histogram is generated by counting the occurrence frequency of the pair of wavelet coefficients. Here, the pair of wavelet coefficients denotes two wavelet coefficients located in the same position in the selected two subbands. In the last step, RDH based on GHS, which embeds the multilevel symbols in the cover image is applied. The experimental results show that the proposed method achieves a higher payload capacity than the conventional RDH methods.

Multispectral filter array and demosaicking for pathological images

A single-shot multispectral camera equipped with a filter array has the potential to promote a fast and low-cost multispectral imaging system. We focus on the design of a multispectral filter array and demosaicking in this paper and propose a pathology-specific multispectral imaging system. The spectral sensitivities and patterns of the filter array are optimized by using training data of real pathological tissues. The mosaicked image is demosaicked by considering the designed filter array. We show the effectiveness of the proposed imaging system by comparing the recovered spectrum and RGB image with conventional methods.

An algorithm to evaluate the number of trabecular cell layers using nucleus arrangement applied to hepatocellular carcinoma

Recent advances in information technology have improved pathological virtual-slide technology and diagnostic support system studies of pathological images. Diagnostic support systems utilize quantitative indices determined by image processing. In previous studies on diagnostic support systems, carcinomatous areas of breast or lung have been recognized by the feature quantities of nuclear sizes, complexities, and internuclear distances based on graph theory, among other features. Improving recognition accuracy is important for the addition of new feature quantities. We focused on hepatocellular carcinoma (HCC) and investigated new feature quantities of histological images of HCC. One of the most important histological features of HCC is the trabecular pattern. For diagnosing cancer, it is important to recognize the tumor cell trabeculae. We propose a new algorithm for calculating the number of cell layers in histological images of HCC in tissue sections stained by hematoxylin and eosin. For the calculation, we used a Delaunay diagram that was based on the median points of nuclei, deleted the sinusoid and fat droplet regions from the Delaunay diagram, and counted the Delaunay lines while applying a thinning algorithm. Moreover, we experimented with the calculation of the number of cell layers with our method for different histological grades of HCC. The number of cell layers discriminated tumor differentiations and Edmondson grades; therefore, our algorithm may serve as an index of HCC for diagnostic support systems.

An efficient compression method for one-shot multispectral camera

Many RGB digital cameras use a Bayer color filter array. In these cameras a full-resolution image is obtained from a mosaicked image by interpolation, and then the full resolution image is encoded. Similarly, mosaicked color filter arrays and interpolation methods for multispectral images have been proposed in recent years. The resulting full-resolution mutispectral images need to be encoded efficiently. This paper presents a new image compression method for one-shot multispectral imaging system. The proposed approach encodes the mosaicked multi-spectral image before interpolation. The decoder interpolates the image after decoding to reconstruct a full-resolution image. The simulation results show that the proposed method outperforms a conventional method, which encodes the full-resolution image after interpolation, at almost all bit rates. We also propose a heuristic color filter array design for our method that leads to lower bit rate.