Chatter is a cause of low surface quality and productivity in milling and crucial features need to be extracted for accurate chatter detection and suppression. This paper introduces a novel feature extraction approach for chatter detection by using image analysis of dominant frequency bands from the short-time Fourier transform (STFT) spectrograms. In order to remove the environmental noises and highlight chatter related characteristics, dominant frequency bands with high energy are identified by applying the squared energy operator to the synthesized fast Fourier transform (FFT) spectrum. The time-frequency spectrogram of the vibration signal is divided into a set of grayscale sub-images according to the dominant frequency bands. Statistical image features are extracted from those sub-images to describe the machining condition and assessed in terms of their separability capabilities. The proposed feature extraction method is verified by using dry milling tests of titanium alloy Ti6Al4V and compared with two existing feature extraction techniques. The results indicate the efficiency of the time-frequency image features from dominant frequency bands for chatter detection and their better performance than the time domain features and wavelet-based features in terms of their separability capabilities.