An Efficient Fractal Method for Detection and Diagnosis of Breast Masses in Mammograms

In this paper, we present an efficient fractal method for detection and diagnosis of mass lesion in mammogram which is one of the abnormalities in mammographic images. We used 110 images that were carefully selected by a radiologist, and their abnormalities were also confirmed by biopsy. These images included circumscribed benign, ill-defined, and spiculated malignant masses. Firstly, we discriminated lesions automatically using new fractal dimensions. The results which were examined by different types of breast density showed that the proposed method was able to yield quite satisfactory detection results. Secondly, noting that contours of masses playing the most important role in diagnosis of different mass types, we defined new fractal features based on information extraction from the contours. This information is able to identify the roughness in mass contours and determines the extent of spiculation or smoothness of the masses. In this manner, in classification of the spiculated malignant masses from the circumscribed benign tumors, we achieved highly satisfactory results, i.e., 0.98 measured in terms of area under ROC curve (AUC). In this paper, it is also shown that the roughness in contours is a suitable characteristic feature for diagnosis of ill-defined malignant tumors with AUC equal to 0.94 in their classification. The extracted information was also found to be useful in the classification of early malignancies whereas in the classification of spiculated and ill-defined malignant masses in their early stage from those of benign tumors, we achieved high accuracy of 0.99 and 0.90 for AUC, respectively.     

Optimization of breast lesion segmentation in texture feature space approach

This paper develops a method for semi-automatic detection of breast lesion boundaries by combining the snake evolution techniques with statistical texture information of images. We propose an efficient image energy function in segmentation based on image features, first-order textural features and four n × n masks. The segmentation results were evaluated by using area error rate. The image features were evaluated qualitatively by using the contrast-to-noise ratio and fractal dimension analysis. In our study, standard deviation, skewness and entropy are indicated as being the most relevant image features.     

A new automatic image analysis method for assessing estrogen receptors’ status in breast tissue specimens

Manual assessment of estrogen receptors′ (ER) status from breast tissue microscopy images is a subjective, time consuming and error prone process. Automatic image analysis methods offer the possibility to obtain consistent, objective and rapid diagnoses of histopathology specimens. In breast cancer biopsies immunohistochemically (IHC) stained for ER, cancer cell nuclei present a large variety in their characteristics that bring various difficulties for traditional image analysis methods. In this paper, we propose a new automatic method to perform both segmentation and classification of breast cell nuclei in order to give quantitative assessment and uniform indicators of IHC staining that will help pathologists in their diagnostic. Firstly, a color geometric active contour model incorporating a spatial fuzzy clustering algorithm is proposed to detect the contours of all cell nuclei in the image. Secondly, overlapping and touching nuclei are separated using an improved watershed algorithm based on a concave vertex graph. Finally, to identify positive and negative stained nuclei, all the segmented nuclei are classified into five categories according to their staining intensity and morphological features using a trained multilayer neural network combined with Fisher's linear discriminant preprocessing. The proposed method is tested on a large dataset containing several breast tissue images with different levels of malignancy. The experimental results show high agreement between the results of the method and ground-truth from the pathologist panel. Furthermore, a comparative study versus existing techniques is presented in order to demonstrate the efficiency and the superiority of the proposed method.     

A unified model of illusory and occluded contour interpolation

Models of contour interpolation have been proposed for illusory contour interpolation but seldom for interpolation of occluded contours. The identity hypothesis ( [Kellman and Loukides, 1987] and [Kellman and Shipley, 1991]) posits that an early interpolation mechanism is shared by interpolated contours that are ultimately perceived as either illusory or occluded. Here we propose a model of such a unified interpolation mechanism for illusory and occluded contours, building on the framework established in Heitger, von der Heydt, Peterhans, Rosenthaler, and Kubler (1998). We show that a single, neurally plausible mechanism that is consistent with the identity hypothesis also generates contour interpolations in agreement with perception for cases of transparency, self-splitting objects, interpolation with mixed boundary assignment, and “quasimodal” interpolations. Limiting cases for this local, feed-forward approach are presented, demonstrating that both early, local interpolation mechanisms and non-local scene constraints are necessary for describing the perception of interpolated contours.     

FDG-PET provides the best correlation with the tumor specimen compared to MRI and CT in rectal cancer

Purpose

To compare CT-, MR- and PET-CT based tumor length measurements in rectal cancer with pathology.

Patients and Methods

Twenty-six rectal cancer patients underwent both MR and PET-CT imaging followed by short-course radiotherapy (RT 5 × 5 Gy) and surgery within 3 days after RT. Tumor length was measured manually and independently by 2 observers on CT, MR and PET. PET-based tumor length measurements were also generated automatically using the signal-to-background-ratio (SBR) method. All measurements were correlated with the tumor length on the pathological specimen.

Results

CT-based measurements did not show a valuable correlation with pathology. MR-based measurements correlated only weakly, but still significantly (Pearson correlation = 0.55 resp. 0.57; p < 0.001). Manual PET measurements reached a good correlation with pathology, but less strong (Pearson correlation 0.72 and 0.76 for the two different observers) than automatic PET-CT based measurements, which provided the best correlation with pathology (Pearson correlation of 0.91 (p < 0.001)). Bland-Altman analysis demonstrated in general an overestimation of the tumor diameter using manual measurements, while the agreement of automatic contours and pathology was within acceptable ranges. A direct comparison of the different modalities revealed a significant better precision for PET-based auto-contours as compared to all other measurements.

Conclusion

Automatically generated PET-CT based contours show the best correlation with the surgical specimen and thus provide a useful and powerful tool to accurately determine the largest tumor dimension in rectal cancer. This could be used as a quick and reliable tool for target delineation in radiotherapy. However, a 3D volume analysis is needed to confirm these results.     

Optimum fuzzy filters for phase‐contrast magnetic resonance imaging segmentation

Purpose

To develop and validate a multidimensional segmentation and filtering methodology for accurate blood flow velocity field reconstruction from phase‐contrast magnetic resonance imaging (PC MRI).

Materials and Methods

The proposed technique consists of two steps: (1) the boundary of the vessel is automatically segmented using the active contour approach; and (2) the noise embedded within the segmented vector field is selectively removed using a novel fuzzy adaptive vector median filtering (FAVMF) technique. This two‐step segmentation process was tested and validated on 111 synthetically generated PC MRI slices and on 10 patients with congenital heart disease.

Results

The active contour technique was effective for segmenting blood vessels having a sensitivity and specificity of 93.1% and 92.1% using manual segmentation as a reference standard. FAVMF was the superior technique in filtering out noise vectors, when compared with other commonly used filters in PC MRI (P < 0.05). The peak wall shear rate calculated from the PC MRI data (248 ± 39 sec^?1), was significantly decreased to (146 ± 26 sec^?1) after the filtering process.

Conclusion

The proposed two‐step segmentation and filtering methodology is more accurate compared to a single‐step segmentation process for post‐processing of PC MRI data. J. Magn. Reson. Imaging 2009;29:155–165. © 2008 Wiley‐Liss, Inc.

     

Skin lesions segmentation and quantification from 3D body''s models

BACKGROUND/AIMS: Characterising large areas of the body has always been problematic. The aim of this article is to test a method to evaluate the developed surface areas of dermatological lesions from a 3D textured model of the body's envelope. METHOD: We applied the active contour method to isolate the lesions. Then, by means of the 3D model obtained, we calculated the area. This was tested on standards of known areas. RESULTS: For the standards, the standard deviation between the calculated and theoretical surfaces was under 3%. CONCLUSION: The results obtained indicate the feasibility of the method for studying the efficiency of dermatological treatment.     

Automatic measurement of dermal thickness from B-scan ultrasound images using active contours

BACKGROUND/PURPOSE: Measurement of dermal thickness is useful in the evaluation of dermo-cosmetics for assessing not only morphological changes but also mechanical properties of this layer. Our aim was first to standardise the manual dermal thickness measurement procedure on B-scan ultrasound images, then to develop an automatic operator independent method to detect the boundaries of the dermis. MATERIAL AND METHODS: The Dermcup 20 MHz B-scan ultrasound system was used. The method used for detecting the boundaries was adapted from active contour algorithms. The innovative aspect of the method consists in an automatic initialization of the first step of the algorithm. To validate the method, we correlated measurements obtained by the manual and automatic approaches from a set of images from different anatomical sites. RESULTS AND CONCLUSION: The results showed for the two measurement methods, 72% of the images were perfectly correlated. The remaining images required manual initialization of the boundaries by a non-expert operator before the active contour process could be used. Subsequent to this semi-automatic procedure, the correlation was very high.     

Chest Wall Implants: Their Use for Pectus Excavatum,Pectoralis Muscle Tears,Poland's Syndrome,and Muscular Insufficiency

Solid customized and prefabricated silicone implants have been used by the author for 15 years in a wide range of chest wall deformities. Chest wall implants are often used in males seeking to augment a muscularly deficient or underdeveloped chest; however, their greatest use has come in a variety of deformities both congenital and acquired, such as pectus excavatum, Poland's Syndrome, and pectoralis muscle tears. The implants can be either customized using a moulage technique or are prefabricated, manufactured implants which can be modified on the operating table to repair the contour deformity. The immediate postoperative problem of seroma and subcutaneous implant ``show' has been minimized by careful planning, gentle technique, deep insertion, improved patient positioning on the operating room table, and the use of oral anti-inflammatory medications. The long-term results of these implants seem very satisfactory. The patients are usualy physically active, and the implants show no long-term sequelae such as seroma, infection, displacement, or rupture.     

Iso-frequency 2-DG contours in the inferior colliculus of the awake monkey

Summary Tone bursts produced bands of selective 2-[¹⁴C]-deoxyglucose labelling in the inferior colliculus (IC) of the awake monkey. Low tone frequencies produced labelling in dorsal regions and high tone frequencies produced labelling in ventral regions. The position of the bands coincided with the position of a single unit with a characteristic frequency, which was the same as the frequency producing the labelling. These findings indicate that the bands of labelling represent iso-frequency contours in IC. The iso-frequency contours extended across most of the nucleus and were oriented from dorsomedially to ventro-laterally at 20–30° from the horizontal and became more vertical anteriorly. The width of the contours was as narrow as 200 m, suggesting that the contours might represent 2 or 3 overlapping cellular laminae.Supported by research grants from the National Health and Medical Research Council of Australia and the Australian Research Grants Scheme