Our study aims at developing a computer-aided diagnosis (CAD) system for fully automatic detection and classification
of pathological lung parenchyma patterns in idiopathic interstitial pneumonias (IIP) and emphysema using multi-detector
computed tomography (MDCT). The proposed CAD system is based on three-dimensional (3-D) mathematical
morphology, texture and fuzzy logic analysis, and can be divided into four stages: (1) a multi-resolution decomposition
scheme based on a 3-D morphological filter was exploited to discriminate the lung region patterns at different analysis
scales. (2) An additional spatial lung partitioning based on the lung tissue texture was introduced to reinforce the spatial
separation between patterns extracted at the same resolution level in the decomposition pyramid. Then, (3) a hierarchic
tree structure was exploited to describe the relationship between patterns at different resolution levels, and for each
pattern, six fuzzy membership functions were established for assigning a probability of association with a normal tissue
or a pathological target. Finally, (4) a decision step exploiting the fuzzy-logic assignments selects the target class of each
lung pattern among the following categories: normal (N), emphysema (EM), fibrosis/honeycombing (FHC), and ground
glass (GDG). According to a preliminary evaluation on an extended database, the proposed method can overcome the
drawbacks of a previously developed approach and achieve higher sensitivity and specificity.
Multi-detector computed tomography (MDCT) has high accuracy and specificity on volumetrically capturing serial
images of the lung. It increases the capability of computerized classification for lung tissue in medical research. This paper
proposes a three-dimensional (3D) automated approach based on mathematical morphology and fuzzy logic for
quantifying and classifying interstitial lung diseases (ILDs) and emphysema. The proposed methodology is composed of
several stages: (1) an image multi-resolution decomposition scheme based on a 3D morphological filter is used to detect
and analyze the different density patterns of the lung texture. Then, (2) for each pattern in the multi-resolution
decomposition, six features are computed, for which fuzzy membership functions define a probability of association with
a pathology class. Finally, (3) for each pathology class, the probabilities are combined up according to the weight assigned
to each membership function and two threshold values are used to decide the final class of the pattern. The proposed
approach was tested on 10 MDCT cases and the classification accuracy was: emphysema: 95%, fibrosis/honeycombing:
84% and ground glass: 97%.
Recently, the whole breast ultrasound (US) is a new advanced screening technique for detecting breast
abnormalities. Because a lot of images are acquired for a case, the computer-aided system is needed to help the
physicians to reduce the diagnosis time. In the automatic whole breast US, the ribs are the pivotal landmark just like the
pectoral muscle in the mammography. In this paper, we develop an automatic rib detection method for the whole breast
ultrasound. The ribs could be helpful to define the screening area of a CAD system to reduce the tumor detection time
and could be used to register different passes for a case. In the proposed rib detection system, the whole breast images
are subsampled at first in order to reduce the computation of rib detection without reducing the detection performance.
Due to the shadowing is occurred under the rib in the whole breast ultrasound images and is the sheet-like structure, the
Hessian analysis and sheetness function are adopted to enhance the sheet-like structure. Then, the orientation
thresholding is adopted to segment the sheet-like structures. In order to remove the non-rib components in the
segmented sheet-like structures, some features of ribs in whole breast ultrasound are used. Thus, the connected
component labeling is applied and then some characteristics such as orientation, length and radius are calculated.
Finally, some criteria are applied to remove non-rib components. In our experiments, there are 65 ribs in 15 test cases
and the 62 ribs have been detected by the proposed system with the detection ratio 95.38%. The ratio of position
difference under 5 mm is 87.10 % and the ratio of length difference under 10 mm is 85.48 %. The results show that the
proposed system almost could detect the ribs in the breast US images and has a good accuracy.
Ultrasound has been an important imaging technique for detecting breast tumors. As opposed to the conventional B-mode
image, the ultrasound elastography is a new technique for imaging the elasticity and applied to detect the stiffness
of tissues. The red region of color elastography indicates the soft tissue and the blue one indicates the hard tissue, and
the harder tissue usually is classified to malignancy. In this paper, we proposed a CAD system on elastography to
measure whether this system is effective and accurate to classify the tumor into benign and malignant. According to the
features of elasticity, the color elastography was transferred to HSV color space and extracted meaningful features from
hue images. Then the neural network was utilized in multiple features to distinguish tumors. In this experiment, there
are 180 pathology-proven cases including 113 benign and 67 malignant cases used to examine the classification. The
results of the proposed system showed an accuracy of 83.89%, a sensitivity of 85.07% and a specificity of 83.19%.
Compared with the physician's diagnosis, an accuracy of 78.33%, a sensitivity of 53.73% and a specificity of 92.92%,
the proposed CAD system had better performance. Moreover, the agreement of the proposed CAD system and the
physician's diagnosis was calculated by kappa statistics, the kappa 0.54 indicated there is a fair agreement of observers.
Angiogenesis is the process that correlates to tumor growth, invasion, and metastasis. Breast cancer angiogenesis has been the most extensively studied and now serves as a paradigm for understanding the biology of angiogenesis and its effects on tumor outcome and patient prognosis. Most studies on characterization of angiogenesis focus on pixel/voxel counts more than morphological analysis. Nevertheless, in cancer, the blood flow is greatly affected by the morphological changes, such as the number of vessels, branching pattern, length, and diameter. This paper presents a computer-aided diagnostic (CAD) system that can quantify vascular morphology using 3-D power Doppler ultrasound (US) on breast tumors. We propose a scheme to extract the morphological information from angiography and to relate them to tumor diagnosis outcome. At first, a 3-D thinning algorithm helps narrow down the vessels into their skeletons. The measurements of vascular morphology significantly rely on the traversing of the vascular trees produced from skeletons. Our study of 3-D assessment of vascular morphological features regards vessel count, length, bifurcation, and diameter of vessels. Investigations into 221 solid breast tumors including 110 benign and 111 malignant cases, the p values using the Student's t-test for all features are less than 0.05 indicating that the proposed features are deemed statistically significant. Our scheme focuses on the vascular architecture without involving the technique of tumor segmentation. The results show that the proposed method is feasible, and have a good agreement with the diagnosis of the pathologists.
Early detection through screening is the best defense against morbidity and mortality from breast cancers. Mammography is the most used screening tool for detecting early breast cancer because it can easily obtain the view of whole breast. However, because the ultrasound images are cross-sectional images, not projection images like mammography, and the ultrasound probe does not fully cover the breast width, it is not a convenient screening tool when adjunct with screening mammography. The physician needs a lot of examination time to perform the breast screening. Recently, some whole breast ultrasound scanning machines are developed. The examination could be performed by an experienced technician. Because the probe width still does not fully cover the breast width, several scanning passes are required to obtain the whole breast image. The physician still cannot have a full view of breast. In this paper, an image stitching technique is proposed to stitch multi-pass images into a full-view image. The produced full-view image can reveal the breast anatomy and assists physicians to reduce extra manual adjustment.
Among the image coding techniques, vector quantization (VQ) has been considered to be an effective method for coding images at low bit rate. Side-match finite-state vector quantizer (SMVQ) exploits the correlations between the neighboring blocks (vectors) to avoid large gray level transition across block boundaries. In this paper, an improved SMVQ technique named two-pass side-match finite-state vector quantization (TPSMVQ) has been proposed. In TPSMVQ, the size of state codebook in the first pass is decided by the variances of neighboring blocks. In the second pass, we will improve the blocks encoded in the first pass whose variances are greater than a threshold. Moreover, not only the left and upper blocks but also the down and right blocks are used for constructing the state codebook. In our experiment results, the improvement of second pass is up to 1.5 dB in PSNR over the fist pass. In comparison to ordinary SMVQ, the improvement is upt to 1.54 dB at nearly the same bit rate.
Vector quantization (VQ) is an effective image coding technique at low bit rate. Side-match finite-state vector quantizer (SMVQ) exploits the correlations between the neighboring blocks (vectors) to avoid large gray level transition across block boundaries. In this paper, a new adaptive quadtree-based side-match finite-state vector quantizer (QBSMVQ) has been proposed. In QBSMVQ, the blocks are classified into two main classes, edge blocks and nonedge blocks, to avoid selecting a wrong state codebook for an input block. In order to improve the image quality, edge vectors are reclassified into sixteen classes. Each class uses a master codebook that is different from the codebook of other classes. In our experiments, results are given and comparisons are made between the new scheme and ordinary SMVQ coding techniques. As will be shown, the improvement of QBSMVQ over the ordinary SMVQ is up to 3.13 dB at nearly the same bit rate. Moreover, the improvement over the ordinary VQ can be up to 4.30 dB at the same bit rate for the image Lena. Further, block boundaries and edge degradation are less visible because of edge-vector classification. Hence, the perceived improvement in quality over ordinary SMVQ is even greater for human sight.
Subband coding and vector quantization have been shown to be effective methods for coding images at low bit rates. In this paper, we propose a new subband finite-state vector quantization scheme that combines the SBC and FSVQ. A frequency band decomposition of the image is carried out by means of 2D separable quadrature mirror filters, which split the image spectrum into 16 subbands. In general, the 16 subbands can be encoded by intra-band VQ or inter-band VQ. We will use the inter-band VQ to exploit the correlations among the subband images. Moreover, the FSVQ is used to improve the performance by using the correlations of the neighboring samples in the same subband. It is well known that the inter- band VQ scheme has several advantages over coding each subband separately. Our subband- FSVQ scheme not only has all the advantages of the inter-band VQ scheme but also reduces the bit rate and improves the image quality. Comparisons are made between our scheme and some other coding techniques. The new scheme yields a good peak signal-to-noise ratio performance in the region between 0.30 and 0.31 bit per pixel, both for images inside and outside a training set of five 512 X 512 mono-chrome images. In the experiments, the improvement of our scheme over the ordinary VQ without SBC is up to 3.42 dB and over the inter-band VQ is up to 1.20 dB at nearly the same bit rate for the image Lena. The PSNR of the encoded image Lena using the proposed scheme is 32.1 dB at 0.31 bit per pixel.
The Linde-Buzo-Gray (LBG) algorithm is usually used to design a codebook for encoding images in the vector quantization. In each iteration of this algorithm, we must search the full codebook in order to assign the training vectors to their corresponding codewords. Therefore, the LBG algorithm needs large computation effort to obtain a good codebook from the training set. In this paper, we propose a finite-state LBG (FSLBG) algorithm for reducing the computation time. Instead of searching the whole codebook, we search only those codewords that are close to the codeword for a training vector in its previous iteration. In general, the number of these possible codewords can be very small without sacrificing performance. Because of searching only a small part of the codebook, the computation time is reduced. In our experiment, the performance of the FSLBG algorithm in terms of the signal-to-noise ratio is very close to that of the LBG algorithm. However, the computation time of the FSLBG algorithm is only about 10 percent of the time required by the LBG algorithm.