Computerized Determination Scheme for Histological Classification of Breast Mass Using Objective Features Corresponding to Clinicians’ Subjective Impressions on Ultrasonographic Images

Abstract

It is often difficult for clinicians to decide correctly on either biopsy or follow-up for breast lesions with masses on ultrasonographic images. The purpose of this study was to develop a computerized determination scheme for histological classification of breast mass by using objective features corresponding to clinicians' subjective impressions for image features on ultrasonographic images. Our database consisted of 363 breast ultrasonographic images obtained from 363 patients. It included 150 malignant (103 invasive and 47 noninvasive carcinomas) and 213 benign masses (87 cysts and 126 fibroadenomas). We divided our database into 65 images (28 malignant and 37 benign masses) for training set and 298 images (122 malignant and 176 benign masses) for test set. An observer study was first conducted to obtain clinicians' subjective impression for nine image features on mass. In the proposed method, location and area of the mass were determined by an experienced clinician. We defined some feature extraction methods for each of nine image features. For each image feature, we selected the feature extraction method with the highest correlation coefficient between the objective features and the average clinicians' subjective impressions. We employed multiple discriminant analysis with the nine objective features for determining histological classification of mass. The classification accuracies of the proposed method were 88.4 % (76/86) for invasive carcinomas, 80.6 % (29/36) for noninvasive carcinomas, 86.0 % (92/107) for fibroadenomas, and 84.1 % (58/69) for cysts, respectively. The proposed method would be useful in the differential diagnosis of breast masses on ultrasonographic images as diagnosis aid.

Brightness and Contrast Modification in Ultrasonography Images Using Edge Detection Results

“…Wang [14] investigated and compared contrast-enhanced ultrasound (CEUS) in the characterisation of histologically proven focal nodular hyperplasia (FNH) with contrast-enhanced computed tomography (CECT). Hizukuri [15] developed a computerized determination scheme for histological classification of breast mass by using objective features corresponding to clinicians' subjective impressions for image features on ultrasonographic images. According to these researches, it seems that most of researcher using complex method to optimize ultrasonography function.…”

Computerized Scheme for Histological Classification of Masses with Architectural Distortions in Ultrasonographic Images

“…clinician. We then extracted five objective features for architectural distortion and nine objective features for masses defined in our previous study [13]. We finally employed the k-NN rule using the extracted objective features to determine the histological classifications of the masses with architectural distortion.…”

“…For accurate extraction of image features, the locations and shapes of all masses were determined by an experienced clinician. Table 1 shows all 14 objective features that were extracted, consisting of five objective features for architectural distortion and nine objective features for mass [13]. The asterisk indicates that the features were newly defined in this study.…”

“…In past work, we had proposed nine objective features for the histological classification of masses in ultrasonographic images [13]. These features reflected clinicians' subjective impressions based on experience.…”

“…To evaluate the mass with architectural distortion in ultrasonographic image, it is necessary to develop the feature extraction method to analyze both feature of mass and feature of architectural distortion. In a past study [13], we developed a CADx system that could evaluate the likelihood of malignancy and that of the histological classification of masses in ultrasonographic images. However, our CADx system did not analyze the objective features of the architectural distortion.…”