Exploring the interior of 3D endoluminal lesions in the air spaces by a novel electronic biopsy technique: A preliminary study of endoluminal colon lesions.

To explore the interior of a lesion in a 3D endoluminal view, this study investigates the application of an 'electronic biopsy' (EB) technique to computed tomographic colonography (CTC) for further differentiation and 2D image correlation of endoluminal lesions in the air spaces. A retrospective study of sixty-two various endoluminal lesions from thirty patients (13 males, 17 females; age range, 31 to 90 years) was approved by our institutional review board and evaluated. The endoluminal lesions were segmented using gray-level threshold and reconstructed into isosurfaces using a marching cube algorithm. EB allows users to interactively erode and apply grey-level mapping (GM) to the surface of the region of interest (ROI) in 3D CTC. Radiologists conducted the clinical evaluation, and the resulting data were analyzed. EB significantly improves 3D gray-level presentation for evaluating the surface and inside of endoluminal lesions over that of SR, GM or target GM (TGM) (P < 0.01) with preservation of the 3D spatial effect. Moreover, 3D to 2D image correlation were achieved in any layer of the lesion using EB as did GM/TGM on the surface. The specificity and diagnostic accuracy of EB are significantly greater than those of SR (P < 0.01). These performance can be better further with GM/TGM and reach the best with EB (specificity, 89.3-92.9%; accuracy, 95.2-96.8%). EB can be used in CTC to improve the differentiation of endoluminal lesions. EB increases 3D to 2D image correlations of the lesions on or beneath the lesion surface.

Improving Image Correlation and Differentiation of 3D Endoluminal Lesions in the Air Spaces Using a Novel Target Gray Level Mapping Technique: A Preliminary Study of Its Application to Computed Tomographic Colonography and Comparison with Traditional Surface Rendering Method.

Purpose: To improve the three dimensional (3D) and two dimensional (2D) image correlation and differentiation of 3D endoluminal lesions in the traditional surface rendering (SR) computed tomographic endoscopy (CTE), a target gray level mapping (TGM) technique is developed and applied to computed tomographic colonography (CTC) in this study. Methods: A study of sixty-two various endoluminal lesions from thirty patients (13 males, 17 females; age range 31-90 years) was approved by our institutional review board and evaluated retrospectively. The endoluminal lesions were segmented using gray level threshold. The marching cubes algorithm was used to detect isosurfaces in the segmented volumetric data sets. TGM allows users to interactively apply grey level mapping (GM) to region of interest (ROI) in the 3D CTC. Radiologists conducted the clinical evaluation and the resulting data were analyzed. Results: TGM and GM are significantly superior to SR in terms of surface texture, 3D shape, the confidence of 3D to 2D, 2D to 3D image correlation, and clinical classification of endoluminal lesions (P < 0.01). The specificity and diagnostic accuracy of GM and TGM methods are significantly better than those of SR (P < 0.01). Moreover, TGM performs better than GM (specificity: 75.0-85.7% vs. 53.6-64.3%; accuracy: 88.7-93.5% vs. 77.4-83.9%). TGM is a preferable display mode for further localization and differentiation of a lesion in CTC navigation. Conclusions: Compared with only the spatial shape information in traditional SR of CTC images, the 3D shapes and gray level information of endoluminal lesions can be provided by TGM simultaneously. 3D to 2D image correlations are also increased and facilitated at the same time. TGM is less affected by adjacent colon surfaces than GM. TGM serves as a better way to improve the image correlation and differentiation of endoluminal lesions.

SVM-Enabled Intelligent Genetic Algorithmic Model for Realizing Efficient Universal Feature Selection in Breast Cyst Image Acquired via Ultrasound Sensing Systems.

In recent years, there are several cost-effective intelligent sensing systems such as ultrasound imaging systems for visualizing the internal body structures of the body. Further, such intelligent sensing systems such as ultrasound systems have been deployed by medical doctors around the globe for efficient detection of several diseases and disorders in the human body. Even though the ultrasound sensing system is a useful tool for obtaining the imagery of various body parts, there is always a possibility of inconsistencies in these images due to the variation in the settings of the system parameters. Therefore, in order to overcome such issues, this research devises an SVM-enabled intelligent genetic algorithmic model for choosing the universal features with four distinct settings of the parameters. Subsequently, the distinguishing characteristics of these features are assessed utilizing the Sorensen-Dice coefficient, T-test, and Pearson's R measure. It is apparent from the results of the SVM-enabled intelligent genetic algorithmic model that this approach aids in the effectual selection of universal features for the breast cyst images. In addition, this approach also accomplishes superior accuracy in the classification of the ultrasound image for four distinct settings of the parameters.

Application of gray level mapping in computed tomographic colonography: a pilot study to compare with traditional surface rendering method for identification and differentiation of endoluminal lesions.

OBJECTIVE: In traditional surface rendering (SR) computed tomographic endoscopy, only the shape of endoluminal lesion is depicted without gray-level information unless the volume rendering technique is used. However, volume rendering technique is relatively slow and complex in terms of computation time and parameter setting. We use computed tomographic colonography (CTC) images as examples and report a new visualization technique by three-dimensional gray level mapping (GM) to better identify and differentiate endoluminal lesions. METHODS: There are 33 various endoluminal cases from 30 patients evaluated in this clinical study. These cases were segmented using gray-level threshold. The marching cube algorithm was used to detect isosurfaces in volumetric data sets. GM is applied using the surface gray level of CTC. Radiologists conducted the clinical evaluation of the SR and GM images. The Wilcoxon signed-rank test was used for data analysis. RESULTS: Clinical evaluation confirms GM is significantly superior to SR in terms of gray-level pattern and spatial shape presentation of endoluminal cases (p < 0.01) and improves the confidence of identification and clinical classification of endoluminal lesions significantly (p < 0.01). The specificity and diagnostic accuracy of GM is significantly better than those of SR in diagnostic performance evaluation (p < 0.01). CONCLUSION: GM can reduce confusion in three-dimensional CTC and well correlate CTC with sectional images by the location as well as gray-level value. Hence, GM increases identification and differentiation of endoluminal lesions, and facilitates diagnostic process. Advances in knowledge: GM significantly improves the traditional SR method by providing reliable gray-level information for the surface points and is helpful in identification and differentiation of endoluminal lesions according to their shape and density.

Tumor rupture as an initial manifestation of malignant mesonephric mixed tumor: a case report and review of the literature.

Malignant mesonephric mixed tumor (MMMT), or mesonephric carcinosarcoma, is a rare tumor with malignant epithelial and mesenchymal components, and is found mostly in the uterine cervix. While diagnosed at the early stage in most cases, MMMT can have an aggressive course. The clinical significance of the presence of sarcomatous components remains unsettled. We report a case of MMMT of the uterine cervix in a patient who presented with tumor rupture, instead of the common presentation, vaginal bleeding. This unusual presentation has not been reported in the literature. It implies that MMMT may progress rapidly without any prodrome and pose a surgical emergency. Unlike most cervical adenocarcinomas, both mesonephric adenocarcinoma and MMMT are not related to human papilloma virus (HPV) infection. Because mesonephric neoplasms have a different etiology, their prevention, screening, and treatment should be further investigated. Thirteen cases of MMMT reported in the literature are also reviewed.

Characterizing the major sonographic textural difference between metastatic and common benign lymph nodes using support vector machine with histopathologic correlation.

Sonographic texture analysis can reflect histopathological components and their arrangement in metastatic and common benign lymph nodes. It is helpful in differentiation between metastatic and benign lymph node lesions for target selection during biopsy of multiple lymph nodes and the strategy of the management. Two ultrasound systems, 107 sonographic regions of interest (ROIs) of metastases and 174 sonographic ROIs of common benign lymph nodes, were recruited in the study. Thirteen texture features derived from co-occurrence matrix were used in characterization of above ROI ultrasound images. Support vector machine (SVM) was used as a classifier and a feature selector. The experimental results show that the entropy gains the best cross-validation accuracy of 94.66% and 87.73% in both ultrasound systems 1 and 2 for the classification of metastatic and benign lymph nodes disease. The accuracy can be further increased to 97.86% and 100% by the combination of the sum average in the study. There are significantly higher entropy and sum average values of the metastatic lymph nodes than of the benign lymph nodes, which are due to the heterogeneous compositions and arrangement of larger cancer cells, lymphocytes, and stroma in metastatic lymph nodes that contrast with simple inflammatory cells infiltration in common benign lymph nodes.

Diagnostic value of I-131 NP-59 SPECT/CT scintigraphy in patients with subclinical or atypical features of primary aldosteronism.

Accumulating evidence has shown the adverse effect of long-term hyperaldosteronism on cardiovascular morbidity that is independent of blood pressure. However, the diagnosis of primary aldosteronism (PA) remains a challenge for patients who present with subtle or atypical features or have chronic kidney disease (CKD). SPECT/CT has proven valuable in the diagnosis of a number of conditions. The aim of this study was to determine the usefulness of I-131 NP-59 SPECT/CT in patients with atypical presentations of PA and in those with CKD. The records of 15 patients with PA were retrospectively analyzed. NP-59 SPECT/CT was able to identify adrenal lesion(s) in CKD patients with suspected PA. Patients using NP-59 SPECT/CT imaging, compared with those not performing this procedure, significantly featured nearly normal serum potassium levels, normal aldosterone-renin ratio, and smaller adrenal size on CT and pathological examination and tended to feature stage 1 hypertension and non-suppressed plasma renin activity. These findings show that noninvasive NP-59 SPECT/CT is a useful tool for diagnosis in patients with subclinical or atypical features of PA and those with CKD.

Classification of the thyroid nodules based on characteristic sonographic textural feature and correlated histopathology using hierarchical support vector machines.

In this study, the ultrasound images of thyroid nodules were classified to facilitate clinical diagnosis and management. The hierarchical support vector machines (SVM) classification system was used to select the characteristic sonographic textural feature that represents the major histopathologic components of the thyroid nodules. Two ultrasound systems (LA39 and i12L mentioned in the Materials and Methods section) were used for comparison. Seventy-six thyroid nodular lesions and 157 regions-of-interest thyroid ultrasound image from each system were recruited in the study. The parameters affecting image acquisition were kept in the same condition for all lesions. Commonly used texture analysis methods were applied to characterize thyroid ultrasound images. Image features were classified according to the corresponding pathologic findings. To estimate their relevance and performance to classification, SVMs were used as a feature selector and a classifier. The thyroid nodules are first categorized as two main types, i.e., follicle base and fibrosis base nodule, by sum average. The follicle base nodules can be further and completely classified into follicles with few cells, follicles with follicular cells and follicles with papillary cancer cells by run length nonuniformity (RLNU). The fibrosis base nodules are further classified by sum square into fibrosis with few cells and fibrosis with dominant cells. The fibrosis base neoplasm with dominant cells can be separated into fibrosis with follicular cells and fibrosis with papillary cancer cells by entropy. The hierarchical SVM classification system achieves a diagnostic accuracy between 96.34% and 100%. Besides, the best sonographic textural feature can be selected by the system for the differentiation between the follicle and fibrosis base thyroid nodules or the cell types mixed in them. In follicle base thyroid nodules, papillary cancers show higher sonographic textural RLNU but less than follicular cells. In fibrosis base thyroid nodules, papillary cancers show increased sonographic textural variance and entropy.

Characterization of the major histopathological components of thyroid nodules using sonographic textural features for clinical diagnosis and management.

In this study, the characteristic sonographic textural feature that represents the major histopathologic components of the thyroid nodules was objectively quantified to facilitate clinical diagnosis and management. A total of 157 regions-of-interest thyroid ultrasound image was recruited in the study. The sonographic system used was the GE LOGIQ 700), (General Electric Healthcare, Chalfant St. Giles, UK). The parameters affecting image acquisition were kept in the same condition for all lesions. Commonly used texture analysis methods were applied to characterize thyroid ultrasound images. Image features were classified according to the corresponding pathologic findings. To estimate their relevance and performance to classification, ReliefF was used as a feature selector. Among the various textural features, the sum average value derived from co-occurrence matrix can well reflect echogenicity and can effectively differentiate between follicles and fibrosis base thyroid nodules. Fibrosis shows lowest echogenicity and lowest difference sum average value. Enlarged follicles show highest echogenicity and difference sum average values. Papillary cancer or follicular tumors show the difference sum average values and echogenicity between. The rule of thumb for the echogenicity is that the more follicles are mixed in, the higher the echo of the follicular tumor and papillary cancer will be and vice versa for fibrosis mixed. Areas with intermediate and lower echo should address the possibility of follicular or papillary neoplasm mixed with either follicles or fibrosis. These areas provide more cellular information for ultrasound guided aspiration

Quantitative correlation between sonographic textural feature and histopathological components for breast cancer: preliminary results.

In this study, the sonographic texture and the histopathological features of breast cancer were objectively characterized. Textural dissimilarity is demonstrated to correlate well with the corresponding histopathological components. The normalized percentage of both fibrosis area and cellular area has highly linear correlation with the textural feature of dissimilarity. The correlation coefficients are -.880 and .857, respectively. The cancerous region with increased fibrous tissues shows low textural dissimilarity and has a strong tendency of negative correlation, whereas the cancerous region with increased cellularity exhibits high textural dissimilarity and a good positive correlation. These results have not been reported so far, and they can be used to predict cellular and fibrotic portions of breast cancer for biopsy or surgery planning, disease progression monitoring, and therapeutic effect evaluation. The proposed image analysis method may also be extended to similar characterization of cancerous tissue in other applications.

The image feature analysis for microscopic thyroid tissue classification.

Thyroid diseases are prevalent among endocrine diseases. Observation and examination of histological tissue images can help in understanding the cause and pathogenesis of the tumor. The aim of this study was to quantify the histological image features of microscopic thyroid images in order to classify varying tissue types. Five typical histological thyroid tissues were characterized using seven image features including hue, brightness, standard deviation of brightness, entropy, energy, regularity, and fractal analysis. Statistical stepwise selection and multiple discriminant analysis were then used to classify the features. The results show all of the features are significant and our algorithm has the capability of differentiating histological tissue types. The algorithm is applied utilizing quad-tree based region splitting methods to segment the tissue regions from the heterogeneous microscopic image. The preliminary results show the system has good performance for tissue segmentation.

Quantitatively characterizing the textural features of sonographic images for breast cancer with histopathologic correlation.

OBJECTIVE: In this study, quantitative characterization of sonographic image texture and its correlation with histopathologic findings was developed for facilitating clinical diagnosis. A statistical feature matrix was applied to quantify the texture difference (ie, the dissimilarity) of the sonographic images for malignant and benign breast tumors. METHODS: Thirty-three patients were recruited for this study. Imaging was performed on a commercially available sonographic imaging system in clinical use. The parameters used for image acquisition were kept the same during clinical examination. RESULTS: On the basis of dissimilarity values, 3 phenomena were noted in the relatively large malignancies studied. First, stellate carcinoma showed the least dissimilarity on sonographic images; second, circumscribed carcinoma showed the most dissimilarity; and third, malignant tissue mixed with fibrous and cellular parts (dense lymphocyte infiltration and prominent intraductal tumors) had dissimilarity values in between. Image textures with smaller dissimilarity values (especially for those values <4.4 in our study) are likely to be stellate carcinoma. CONCLUSIONS: From the experimental results, it is shown that the cellular and fibrous content with spatial distribution of breast masses determine the dissimilarity values on sonographic images. The dissimilarity may be used to quantitatively represent the image texture and is well correlated with the histopathologic description.