Region of interest (ROI) coding is a useful function for many applications. JPEG2000 supports ROI coding and can decode ROIs preferentially regardless of the shape and number of the regions. However, if the number of regions is quite large, the ROI coding performance of JPEG2000 declines because the code-stream includes many useless non-ROI codes. This paper proposes a wavelet-based ROI coding method suited for multiple ROIs. The proposed wavelet transform does not access any non-ROIs when transforming the ROIs. Additionally, the proposed method eliminates the need for unnecessary coding of the bits in the higher bit planes of non-ROI regions by adding an ROI map to the code-stream. The experimental results show that the proposed method achieves a higher peak signal-to-noise ratio than the ROI coding of JPEG2000. The proposed method can be applied to both max-shift and scaling-based ROI coding.

Histopathology is a microscopic anatomical study of body tissues and widely used as a cancer diagnosing method. Generally, pathologists examine the structural deviation of cellular and sub-cellular components to diagnose the malignancy of body tissues. These judgments may often subjective to pathologists' skills and personal experiences. However, computational diagnosis tools may circumvent these limitations and improve the reliability of the diagnosis decisions. This paper proposes a prostate image classification method by extracting textural behavior using multifractal analysis. Fractal geometry is used to describe the complexity of self-similar structures as a non-integer exponent called fractal dimension. Natural complex structures (or images) are not self-similar, thus a single exponent (the fractal dimension) may not be adequate to describe the complexity of such structures. Multifractal analysis technique has been introduced to describe the complexity as a spectrum of fractal dimensions. Based on multifractal computation of digital imaging, we obtain two textural feature descriptors; i) local irregularity: α and ii) global regularity: f(α). We exploit these multifractal feature descriptors with a texton dictionary based classification model to discriminate cancer/non-cancer tissues of histopathology images of H&E stained prostate biopsy specimens. Moreover, we examine other three feature descriptors; Gabor filter bank, LM filter bank and Haralick features to benchmark the performance of the proposed method. Experiment results indicated that the performance of the proposed multifractal feature descriptor outperforms the other feature descriptors by achieving over 94% of correct classification accuracy.