The present CAD method using texture analysis to analyze the distribution/heterogeneity of SUV and CT values for malignant and benign bone and soft-tissue lesions improved the differential diagnosis on (18)F-FDG PET/CT images.

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Abstract. Visual inspection of diffuse lung disease (DLD) patterns on high-resolution computed tomography (HRCT) is difficult because of their high complexity. We proposed a bag of words based method on the classification of these textural patters in order to improve the detection and diagnosis of DLD for radiologists. Six kinds of typical pulmonary patterns were considered in this work. They were consolidation, ground-glass opacity, honeycombing, emphysema, nodular and normal tissue. Because they were characterized by both CT values and shapes, we proposed a set of statistical measure based local features calculated from both CT values and the eigen-values of Hessian matrices. The proposed method could achieve the recognition rate of 95.85%, which was higher comparing with one global feature based method and two other CT values based bag of words methods.

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Objective definition of GGO area by CT attenuation is feasible. This method is useful for semiautomatic differentiation between GGOs and solid types of lung cancer.

Two β‐galaclosidases (β‐Galase‐I and ‐II, EC 3.2.1.23) and two α‐l‐arabinofuranosidases (α‐l‐Arafase‐I and ‐II. EC 3.2.1.55). were purified from mesophyll tissues of spinach (Spinacia oleracea L.), using chromatography on DEAE‐cellulose, lactose‐conjugated Sepharose CL‐4B, and Sephadex G‐100, or on hydroxylapatite and Sephadex G‐150. The apparent molecular mass (Mr) of β‐Galase‐I and ‐II, respectively, were estimated to be 38 000 and 58 000 on SDS‐PAGE and 64 000 and 60 000 on gel‐permeation chromatography, indicating that the former was a dimeric protein. The isoelectric points of β‐Galase‐I and ‐II were 6.9 and 5.2, respectively. Both enzymes hydrolyzed maximally p‐nitrophenyl (PNP) β‐galactoside at pH 4.3, and were activated about 2‐fold in the presence of BSA (100 μg ml−1). The activity of both enzymes was inhibited strongly by heavy metal ions and p‐chloromercuriberszoate (p‐CMB). d‐Galactono‐(1→4)‐lactone and d‐galactal served as potent competitive inhibitors for the enzymes. β‐Galase‐I and ‐II could be distinguished from each other in their relative rates and kinetic properties in the hydrolysis of aryl β‐galactosides as well as of lactose and galacto‐oligosaccharides. In particular. β‐Galase‐I exhibited a preferential exowise cleavage of β‐1,6‐galactotriose and β‐1.3‐galactan. α‐l‐Arafase‐l (Mr 118000) and ‐II (M, 68 000) were optimally active on PNP α‐l‐arabinofuranoside at pH 4.8 and gave Km values of 1.2 and 2.2 mM. respectively. l‐Arabino‐(1 → 4)‐lactone. Ag+, and SDS acted as inhibitors for the isozymes. α‐lArafase‐I was characterized by its activity to hydrolyze PNP β‐d‐xylopyranoside besides PNP α‐l‐arabinofuranoside. inhibition by d‐xylose and d‐glucono‐(1 → 5)‐lactone. and less sensitivity to Hg2+. Cu2+, and p‐CMB. Sugar beet arabinan was hydrolyzed rapidly by α‐lArafase‐II at one‐half the rate for PNP α‐larabinofuranoside, while the polysaccharide was less susceptible to α‐lArafase‐I. A spinach leaf arabinogalactan‐protein was practically resistant to the action of β‐Galases, but its susceptibility to the enzymes increased remarkably after prior hydrolysis with α‐lArafase‐Il.

SUMMARYComputer-aided diagnosis (CAD) systems on diffuse lung diseases (DLD) were required to facilitate radiologists to read highresolution computed tomography (HRCT) scans. An important task on developing such CAD systems was to make computers automatically recognize typical pulmonary textures of DLD on HRCT. In this work, we proposed a bag-of-features based method for the classification of six kinds of DLD patterns which were consolidation (CON), ground-glass opacity (GGO), honeycombing (HCM), emphysema (EMP), nodular (NOD) and normal tissue (NOR). In order to successfully apply the bag-of-features based method on this task, we focused to design suitable local features and the classifier. Considering that the pulmonary textures were featured by not only CT values but also shapes, we proposed a set of statistical measures based local features calculated from both CT values and eigenvalues of Hessian matrices. Additionally, we designed a support vector machine (SVM) classifier by optimizing parameters related to both kernels and the soft-margin penalty constant. We collected 117 HRCT scans from 117 subjects for experiments. Three experienced radiologists were asked to review the data and their agreed-regions where typical textures existed were used to generate 3009 3D volume-of-interest (VOIs) with the size of 32×32×32. These VOIs were separated into two sets. One set was used for training and tuning parameters, and the other set was used for evaluation. The overall recognition accuracy for the proposed method was 93.18%. The precisions/sensitivities for each texture were 96.67%/95.08% (CON), 92.55%/94.02% (GGO), 97.67%/99.21% (HCM), 94.74%/93.99% (EMP), 81.48%/86.03%(NOD) and 94.33%/90.74% (NOR). Additionally, experimental results showed that the proposed method performed better than four kinds of baseline methods, including two state-of-the-art methods on classification of DLD textures.