MULTI-RESOLUTION HISTOGRAM TECHNIQUE DATA

Mammographic Density Classification Using Multiresolution Histogram Technique – Izzati Muhimmah, Erika R.E. Denton, Reyer Zwiggelaar ISSN 1858-1633 2005 ICTS 21 different risks. On the other hand, a recently published paper by Hadjidemetriou et al. showed that different generic texture images with similar histograms can be discriminated by a multi-resolution approach [5]. Based on these findings, our aim was to investigate whether it is possible to automatically classify mammographic density using a multi-resolution histogram technique. The remainder of this paper is outlined as follows: the proposed multi-resolution histogram features are described in Section 2. Data to validate this methodology are explained in Section 3 and its statistical analysis method is described in Section 4. Section 5 gives results of the proposed method and discussion on our findings. Finally, conclusions appear in Section 6.

2. MULTI-RESOLUTION HISTOGRAM TECHNIQUE

The main aim is to obtain feature vectors which can be used to discriminate between the various mammographic density classes. A feature vector representing a mammogram is derived from a set of histograms {h , h 1 , h 2 , h 3 }, see Figure 2b. h is obtained from the original mammogram, and histograms h 1 , h 2 and h 3 are obtained after Gaussian Filtering the mammogram by 5x5 kernels and scaling in three stages. For all four histograms only grey level information from the breast area ignoring the pectoral muscle and background areas is used and the histograms are normalized with respect to this area. For increasing scales this shows the general shift to lower grey-level values and the narrowing of the peaks in the histogram data. It should be noted that these histograms deviate significantly from those described by Hadjidemetriou et al. [5] which start with delta function peaks which broaden on smoothing. Subsequently, the set of histograms are transformed into a set of cumulative histograms {c , c 1 , c 2 , c 3 }, see Figure 2c. The feature vector for each mammogram is constructed from the difference between subsequent cumulative histograms: {c – c 1 , c 1 - c 2 , c 2 - c 3 }. See Fig. 2d for an example. Between scales this shows a shift to lower grey-level values, but the overall shape of the data remains more or less constant. The dimensionality of the resulting feature space is equal to 768. a b c d Figure 2. Illustration of features selection process: a Example of ROI m29592L, b Histograms of a and its consecutive multi- resolution images, c Cumulative histogram of b, and d Difference of consecutive cumulative histogram c form the classification features

3. DATA

The above technique was evaluated on the dataset comprised sixty post 1998 mammograms from the UK NHS breast screening programme EPIC database, randomly selected representing the Boyds SCC [1] as classified by an expert radiologist. All mammograms are Fuji UMMA filmscreen combinations, medio- lateral views, and digitized using mobile-phone-CCD scanner with 8 bit per pixel accuracy. The breast areas are segmented using threshold and morphological operations, see Figure 2 a for an example. It should be noted that these images are pair mammograms LR from thirty patients.

4. VALIDATION STRATEGY