Automatic detection of abnormalities in mammograms

Our research mainly focuses on low-cost processing mammogram images [1‐5] that results in the segmentation of both abnormal mass and calcification. Statistics show that 30–50 % of cancer has microcalcification and an abnromal mass is also a clear symptom of breast cancer. Thus, early detection of such abnormal mass and microcalcification can help radiologists in better diagnostics, resulting in proper and timely treatment of patients. Segmentation of mammograms for identifying calcification and other masses is an active area of research [6‐9].

I. N. Bankman and T. Nizialek [6] present a new algorithm that is independent of the parametric measurement of the targeted data. They use multitolerance region growing and active contour model for segmentation of mammograms. They does not use any manual threshold or window selection for the detection process. Unfortunately, their approach for segmentation required much time. In some cases, their algorithm reported somewhat larger area compared to the ground truth. Another region growing method is proposed by S. A. Hojjatoleslami and J. Kittler [10]. They use average and peripheral contrast to control the growing process. The major feature of their approach is that at each step only one pixel exhibits the properties to join the region. Unlike the threshold based segmentation, this approach does not necessarily include all the pixels with the same gray level in the region.

AP. Stefanoyiannis et al. [11] propose a method to improve breast periphery in mammogram images by using wavelet-based fusion techniques. This technique results in mammogram images with improved contrast. They use both thresholding and wavelet-based fusion to overcome the overexposure problem of breast periphery during mammography imaging. A. Papadopoulosa [12] assessed the effect of image enhancement and parameters tuning for microcalcification detection. They compared five major image enhancement techniques for CAD assessment, including Contrast-Limited Adaptive Histogram Equalization (CLAHE) and the Local Range Modification (LRM). They report the results of each technique with respect to microcalcification detection and conclude that the LRM algorithm, in most cases, exhibits the best performance but with the selection of appropriate values of the tuning parameters.

I.K Maitra et al. [13] propose Binary Homogenity Enhancement Alogrithm (BHEA), followed by Seeded Region Growing Algorithm (SRGA) to differentiate between normal and abnormal tissues. Their algorithm proceeds to region growing after differentiating between different regions of breast by applying their proposed Anatomical Segmentation of Breast (ASB) algorithm.

R. Lakshmanan [14] proposes an approach to enhance microcalcification features using zero-crossings of contourlet transform, morphology, and artificial neural network that result in an enhanced image as well as preserves features. They tested their scheme using images from the mini-MIAS database.

Finding object boundaries in noisy images is a difficult task and is an important initial step for many segmentation algorithms. K. Somkantha [15] proposes an algorithm for boundary detection in medical images using edge detection techniques.

A. Wroblewska et al. [9] propose a technique for microcalcification detection based on supervised learning. First, they segment objects within a mammogram using the top-hat and thresholding techniques and then use the selected features of the segmented objects as input to a neural network.

Another technique used in mammogram classification is Wavelet Decomposition. T. C. Wang et al. [8] propose an algorithm for microcalcification detection by decomposing an image into different frequency subbands using wavelet decomposition and then reconstructing the image by taking maximum contribution from the high frequency subbands. In wavelet decomposition, the image is decomposed into different frequency subbands, where the region having microcalcification exhibits relatively high frequency as compared to other areas.

E. Hashemi et al. [16] also use wavelet based microcalcification detection. They show that wavelet features of the image have the property that the reconstruction of the image from major wavelet features have common values of skewness and kurtosis on the intersection of their rows and columns. Therefore, they report the intersection area of significant rows and columns (i.e., rows and columns having the highest values of skewness and kurtosis) to be the strong candidate for microcalcification clusters.

It is observed that calcifications, specifically microcalcification areas, occur as tiny blobs in mammograms. Several approaches exist for blob detection. Some of them are scale invariant. For example, Lindeberge [17] describes a blob detection technique at different scales.

In our paper, we propose a method for calcification detection in general and not specific to mammograms. Because the sizes of microcalcification blobs in mammograms are roughly the same, therefore, detection of blobs in mammograms using scale space theory is an overkill. We propose a simple and efficient method for blob detection at a particular scale guided by mammogram imaging.

Ethics Statement: “This research was approved by the Ethics committee of INMOL hospital [Lahore, Pakistan] before conducting the actual research. Dr. Zeeshan Rashid Mirza was our focal person in the hospital. He provided us all the information regarding the data formats and properties of certain abnormalities.”

It has been observed that an abnormality, specifically microcalcification area, occurs as a tiny blob in mammograms having more brightness, hence intensity, compared to its neighboring pixels [Fig. 1]. We compute energy at each pixel in a mammogram for two different window functions. By taking the ratio of energy computed for a small window (3 × 3) to the energy computed for a large window (11 × 11), we detect the suspicious area containing an abnormality by thresholding the energy ratio to 80 % of maximum energy ratio and by applying intensity threshold steps afterwards. Then, we apply postprocessing on final results using morphological operations. We apply pre-processing steps at the start of the algorithm to filter out normal images, i.e, images having no abnormality. The purpose of applying this filtering is to eliminate the extra processing involved in processing normal images. A block diagram describing the method used is shown in Fig. 2.

The decision of a CAD system can fall into one of the four categories. An image region can be called abnormal (positive) or normal (negative), and a decision can either be correct (true) or incorrect (false). CAD can generate two types of erroneous outputs, i.e., False Positive (FP) and False Negative (FN). True Positive (TP) and True Negative (TN) are the two correct decisions. Two performance measures of a detection system that are related to the decisions identified above are ‘Sensitivity’ and ‘Specificity’. Sensitivity (or Recall) is the probability of a positive test, given that the patient is ill while specificity is the probability of a negative test that the patient is well. High values of sensitivity and specificity are desirable. ‘Accuracy’ and ‘Precision’ are also used for performance evaluation of CAD systems.

To assess the performance of our algorithm, we tested it on 84 images from the DDSM database. 54 (64 %) of these images were normal and 30 (36 %) were cancerous. The analyses of our results are shown in Table 3.

Evaluating the performance of a CAD scheme developed for mammogram classification requires certain criteria for determining TP and FP cluster detection. For the evaluation of our results, genuine clusters of calcification were identified by an expert radiologist. The criteria that we used for counting the number of TP detections is to regard a cluster as correctly detected if three or more pixels are found within the region marked as containing calcification by a radiologist. All other regions, if detected, are considered to be FP.

As shown in Table 3, our algorithm has 91 % sensitivity, 97 % specificity, 93 % accuracy, and 85 % precision. Two expert radiologists¹ found our results to be very satisfactory and reliable.

For each pixel in the image, the RE calculation takes O(window size) time because we detect blobs at a specific scale regardless of the size of the image, i.e., the window size is fixed (constant). Therefore, time complexity of RE computation at each pixel is O(1), which accumulates to O(n) for the image having n pixels. The cost for thresholding for both energy and intensity in also linear in terms of the number of pixels in the image. Therefore, the time complexity of thresholding is O(n).

The refinement of final results includes the morphological operation, as morphology requires convolution of the structuring element over the whole image. Because of fixing the scale, the size of structuring element is also fixed resulting in the time complexity of the total convolution to be O(n). The growth rate of the whole algorithm, T(n), is T(n) = time cost for computing R.E at each pixel + time cost for thresholding + Time cost for morphology =O(n)+O(n)+O(n)=O(n).

Therefore, the algorithm is linear in terms of number of the pixels in the image.

¹ Dr. Zeeshan Rashid Mirza from Institute of Nuclear Medicine and Oncology (INMOL) and Dr. Zia Faruqui from Diagnostic Center, Lahore, Pakistan.